U.S. patent application number 17/023186 was filed with the patent office on 2022-03-17 for plasma chamber with a multiphase rotating modulated cross-flow.
The applicant listed for this patent is Applied Materials, Inc.. Invention is credited to Ajit Balakrishna, James D. Carducci, Kenneth S. Collins, Jason Kenney, Kartik Ramaswamy, Shahid Rauf, Michael R. Rice.
Application Number | 20220084794 17/023186 |
Document ID | / |
Family ID | |
Filed Date | 2022-03-17 |
United States Patent
Application |
20220084794 |
Kind Code |
A1 |
Collins; Kenneth S. ; et
al. |
March 17, 2022 |
PLASMA CHAMBER WITH A MULTIPHASE ROTATING MODULATED CROSS-FLOW
Abstract
Embodiments disclosed herein include a plasma treatment chamber,
comprising one or more sidewalls. A support surface within the one
or more sidewalls holds a workpiece. A first gas injector along the
one or more sidewalls injects a first gas flow in a first direction
generally parallel to and across a surface of the workpiece. A
first pump port along the one or more sidewalls generally opposite
of the first gas injector pumps out the first gas flow. A second
gas injector along the one or more sidewalls injects a second gas
flow in a second direction generally parallel to and across the
surface of the workpiece. A second pump port along the one or more
sidewalls generally opposite of the second gas injector pumps out
the second gas flow.
Inventors: |
Collins; Kenneth S.; (San
Jose, CA) ; Rice; Michael R.; (Santa Clara, CA)
; Carducci; James D.; (Sunnyvale, CA) ; Ramaswamy;
Kartik; (San Jose, CA) ; Balakrishna; Ajit;
(Santa Clara, CA) ; Rauf; Shahid; (Pleasanton,
CA) ; Kenney; Jason; (Campbell, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Applied Materials, Inc. |
Santa Clara |
CA |
US |
|
|
Appl. No.: |
17/023186 |
Filed: |
September 16, 2020 |
International
Class: |
H01J 37/32 20060101
H01J037/32; H01L 21/67 20060101 H01L021/67; C23C 16/52 20060101
C23C016/52; C23C 16/505 20060101 C23C016/505; C23C 16/455 20060101
C23C016/455 |
Claims
1. A plasma treatment chamber, comprising: one or more sidewalls; a
support surface within the one or more sidewalls to hold a
workpiece; a first gas injector along the one or more sidewalls to
inject a first gas flow in a first direction generally parallel to
and across a surface of the workpiece; a first pump port along the
one or more sidewalls generally opposite of the first gas injector
to pump out the first gas flow; a second gas injector along the one
or more sidewalls to inject a second gas flow in a second direction
generally parallel to and across the surface of the workpiece, the
second direction different from the first direction; and a second
pump port along the one or more sidewalls generally opposite of the
second gas injector to pump out the second gas flow.
2. The plasma treatment chamber of claim 1, wherein the plasma
treatment chamber is configured to use the first and second gas
injectors and the first and second pump ports to rotate the first
and second gas flows laterally across the workpiece from the one or
more sidewalls to provide a multiphase rotating crossflow
operation, the multiphase rotating crossflow operation comprising
at least a 2-phase cycle.
3. The plasma treatment chamber of claim 1, wherein the first gas
injector and the second gas injector are located in openings in the
one or more sidewalls.
4. The plasma treatment chamber of claim 1, wherein locations of
the first pump port and the second pump port are vertically offset
from locations of the first gas injector and the second gas
injector.
5. The plasma treatment chamber of claim 1, wherein the first gas
flow and the second gas flow are switched on and off to control gas
flow rotation.
6. The plasma treatment chamber of claim 1, further comprising a
modulating function applied to a flow rate of at least one of the
first and second gas flows or applied to an outlet conductance
caused by at least one of the first and second pump ports.
7. The plasma treatment chamber of claim 1, wherein the plasma
treatment chamber further comprises a third gas injector and an
opposing third pump port to provide a third injector-pump port pair
and a 3-phase rotating crossflow operation.
8. The plasma treatment chamber of claim 1, wherein at least one of
the first gas injector and the second gas injector comprises a
single vent in the one or more sidewalls.
9. The plasma treatment chamber of claim 1, wherein the first gas
injector and the second gas injector comprises a gas injector array
of individual gas injectors.
10. A method of performing a rotating gas cross-flow in a plasma
treatment chamber, the method comprising: during a first phase,
injecting, by a first gas injector, a first gas flow in a first
direction generally parallel to and across a surface of a device,
and pumping out, by a first pump port, the first gas flow from the
plasma treatment chamber, wherein the first gas injector is along
one or more sidewalls of the plasma treatment chamber at a first
location, and the first pump port is along the one or more
sidewalls at a second location generally opposing the first gas
injector; and during a second phase, injecting, by a second gas
injector, a second gas flow in a second direction generally
parallel to and across the surface of the device, and pumping out,
by a second pump port, the second gas flow from the plasma
treatment chamber, wherein the second direction is different from
the first direction, and wherein the second gas injector is along
the one or more sidewalls at a third location, and the second pump
port is along the one or more sidewalls at a fourth location
generally opposing the second gas injector.
11. The method of claim 10 further comprising querying a machine
learning (ML) model to control timing of the first gas flow and the
second gas flow.
12. The method of claim 11 further comprising developing a
semiconductor manufacturing process recipe for the device by:
selecting one or more device outcomes; and querying the ML model to
obtain a process recipe recommendation suitable for obtaining the
device outcomes when processed by the plasma treatment chamber with
the rotating gas cross-flow.
13. The method of claim 12 further comprising executing a design of
experiment (DoE) on a set of wafers to validate the process recipe
recommended by the ML model.
14. The method of claim 13 further comprising receiving as the
process recipe any combination of: temperature, RF source power,
bias power, gas pressure (mTorr), gas flow ramp open times (msec),
gas flow time (msec), gas flow ramp closed and time (msec), gas
flow fraction at various gas injectors, gas composition at various
injectors, gas flow fraction going to various injectors, gas flow
rotation frequency, gas flow composition frequency, gas flow
rate/velocity (pressure gradient), gas flow direction, gas rotation
phase, electron/plasma density, plasma density gradient, electron
temperature, ion current density, plasma potential, sheath electric
field potential, sheath electric field tilt angle, sheath electric
field z-component, mass fraction atomic O, O flux, and Jion current
density to workpiece.
15. A plasma treatment chamber, comprising: one or more sidewalls;
a support within the one or more sidewalls to hold a workpiece; a
first gas injector along the one or more sidewalls at a first
location; a first pump port along the one or more sidewalls at a
second location generally opposing the first gas injector; a second
gas injector along the one or more sidewalls at a third location; a
second pump port along the one or more sidewalls at a fourth
location generally opposing the second gas injector; and a
multiphase rotating cross-flow operation comprising at least: a
first phase comprising injecting, by the first gas injector, a
first gas flow in a first direction generally parallel to and
across a surface of the workpiece, and pumping out, by the first
pump port, the first gas flow; and a second phase comprising
injecting, by the second gas injector, a second gas flow in a
second direction generally parallel to and across the surface of
the workpiece, and pumping out, by the second pump port, the second
gas flow, wherein the second direction is different than the first
direction.
16. The plasma treatment chamber of claim 15, further comprising a
first gas inlet valve coupled to the first gas injector, a second
gas inlet valve coupled to the second gas injector, a first
pressure control valve coupled to the first pump port, and a second
pressure control valve coupled to the second pump port.
17. The plasma treatment chamber of claim 16, further comprising a
controller coupled to the plasma treatment chamber, the controller
configured to: during the first phase, start the first gas flow by
fully opening the first gas inlet valve and partially opening the
second gas inlet valve; and open the first pressure control valve
and close the second pressure control valve.
18. The plasma treatment chamber of claim 17, wherein the
controller is further configured to: begin to close the first gas
inlet valve near a transition between the first phase and the
second phase, and rotate a direction of gas flow by fully opening
the second gas inlet valve to begin the second phase and partially
opening the first gas inlet valve; and open the second pressure
control valve and close the first pressure control valve.
19. A non-transitory computer readable medium having stored thereon
software instructions that, when executed by a processor, cause the
processor to rotate gas cross-flow in a plasma treatment chamber,
by executing the steps comprising: during a first phase, injecting,
by a first gas injector, a first gas flow in a first direction
generally parallel to and across a surface of a device, and pumping
out, by a first pump port, the first gas flow from the plasma
treatment chamber, wherein the first gas injector is along one or
more sidewalls of the plasma treatment chamber at a first location,
and the first pump port is along the one or more sidewalls at a
second location generally opposing the first gas injector; and
during a second phase, injecting, by a second gas injector, a
second gas flow in a second direction generally parallel to and
across the surface of the device, and pumping out, by a second pump
port, the second gas flow from the plasma treatment chamber,
wherein the second gas injector is along the one or more sidewalls
at a third location, and the second pump port is along the one or
more sidewalls at a fourth location generally opposing the second
gas injector.
20. The non-transitory computer readable medium of claim 19 further
comprising querying a machine learning (ML) models to control
timing of the first gas flow and the second gas flow.
Description
FIELD
[0001] Embodiments of the present disclosure pertain to the field
of semiconductor processing and, in particular, a plasma chamber
with rotating modulated cross-flow.
DESCRIPTION OF RELATED ART
[0002] During a plasma etch, deposition or other treatment
processes, a workpiece, such as a semiconductor wafer, is inserted
to a sealed plasma reactor chamber, and gas is injected into the
chamber over the wafer and then pumped from the chamber. Plasma
chambers often comprise (1) a parallel plate capacitively coupled
plasma (CCP) source where one electrode has the workpiece on its
plasma-facing surface and the other electrode has an array of gas
inlet holes (showerhead) in the plasma-facing surface or (2) an
inductively coupled plasma (ICP) or microwave source with a
radio-frequency (RF) window generally opposite and facing the
workpiece, and an array of gas inlet holes in or near the
window.
[0003] With the axisymmetric gas flow approach described above,
pressure & concentration gradients cause center-to-edge
processing differences on the workpiece. In addition, extraneous
plasma may form in gas inlet holes due to proximity to dense plasma
or breakdown due to high electric fields, leading to non-uniformity
changing overtime. More specifically, the gas inlet holes are
typically formed in a plate of material, such as silicon or silicon
carbide. Energetic ions bombarding the edges of the holes can
deform or facet the holes overtime. The deformed holes, in turn,
result in higher intensity plasma that disrupts the plate,
requiring a change in showerheads after some number of hours (e.g.,
600 hrs.). In some applications, approximately $15 of a
semiconductor wafer cost may be allocated just to the costs of the
showerheads.
SUMMARY
[0004] Embodiments disclosed herein include a plasma treatment
chamber, comprising one or more sidewalls. A support surface within
the one or more sidewalls holds a workpiece. A first gas injector
along the one or more sidewalls injects a first gas flow in a first
direction generally parallel to and across a surface of the
workpiece, and a first pump port along the one or more sidewalls
generally opposite of the first gas injector pumps out the first
gas flow. A second gas injector along the one or more sidewalls
injects a second gas flow in a second direction generally parallel
to and across the surface of the workpiece, and a second pump port
along the one or more sidewalls generally opposite of the second
gas injector pumps out the second gas flow.
[0005] Embodiments disclosed herein include a method of performing
a rotating gas cross-flow in a plasma treatment chamber and a
non-transitory computer readable medium having stored thereon
software instructions that, when executed by a processor, cause the
processor to rotate gas cross-flow in a plasma treatment chamber,
by executing the following steps. During a first phase the steps
include, injecting, by a first gas injector, a first gas flow in a
first direction generally parallel to and across a surface of a
device, and pumping out, by a first pump port, the first gas flow
from the plasma treatment chamber, wherein the first gas injector
is along one or more sidewalls of the plasma treatment chamber at a
first location, and the first pump port is along the one or more
sidewalls at a second location generally opposing the first gas
injector. During a second phase the steps include, injecting, by a
second gas injector, a second gas flow in a second direction
generally parallel to and across the surface of the device, and
pumping out, by a second pump port, the second gas flow from the
plasma treatment chamber, wherein the second gas injector is along
the one or more sidewalls at a third location, and the second pump
port is along the one or more sidewalls at a fourth location
generally opposing the second gas injector.
[0006] Embodiments disclosed herein include a plasma treatment
chamber, comprising one or more sidewalls. A support within the one
or more sidewalls to hold a workpiece. A first gas injector is
along the one or more sidewalls at a first location, and a first
pump port is along the one or more sidewalls at a second location
generally opposing the first gas injector. A second gas is injector
along the one or more sidewalls at a third location, and second
pump port is along the one or more sidewalls at a fourth location
generally opposing the second gas injector. A multiphase rotating
cross-flow operation comprises at least a first phase and a second
phase. The first phase comprises injecting, by the first gas
injector, a first gas flow in a first direction generally parallel
to and across a surface of the workpiece, and pumping out, by the
first pump port, the first gas flow. The second phase comprises
injecting, by the second gas injector, a second gas flow in a
second direction generally parallel to and across the surface of
the workpiece, and pumping out, by the second pump port, the second
gas flow.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1A is a diagram illustrating a top view of the plasma
treatment chamber having a multiphase rotating crossflow operation
according to one embodiment.
[0008] FIGS. 1B and 1C illustrate cross-section views of the plasma
treatment chamber in different embodiments.
[0009] FIG. 2A is a schematic of an angled semi-transparent view of
a 3-phase rotating crossflow plasma treatment chamber according to
an embodiment.
[0010] FIG. 2B is a schematic of a top view of the 3-phase rotating
crossflow plasma treatment chamber according to another
embodiment.
[0011] FIG. 2C illustrates a timing diagram for the 3-phase
rotating crossflow operation performed by plasma treatment
chamber.
[0012] FIGS. 2D illustrates an angled view of a top of the chamber
lid showing a gas delivery system there above according to an
embodiment.
[0013] FIG. 2E illustrates an angled cross-section view of the
plasma chamber according to an embodiment.
[0014] FIGS. 2F-2H illustrate angled and cross-sectional views of a
vacuum chamber in which the pump ports are formed according to an
embodiment.
[0015] FIGS. 2I-2K are diagrams illustrating an angled
semi-transparent view of an example inductively coupled plasma
(ICP) chamber having a 3-phase rotating crossflow according to one
embodiment.
[0016] FIG. 3A is a diagram illustrating a top view of a plasma
treatment chamber having a 4-phase rotating crossflow according to
an embodiment.
[0017] FIG. 3B is a diagram illustrating a 4-phase rotating
crossflow operation according to an embodiment.
[0018] FIGS. 3C and 3D are diagram illustrating a 4-phase rotating
crossflow operation with deliberate non-uniform center and edge gas
injection with opposite side port pumping according to a further
aspect of the disclosed embodiments.
[0019] FIG. 3E is a diagram illustrating a single phase of a
multiphase rotating crossflow operation in which at least a portion
of the gas flow is diverted to the sides of the workpiece rather
than a 100% cross-flow across the workpiece according to an
embodiment.
[0020] FIG. 3F is a diagram a single phase of a multiphase cycle
where gas flow is directed across the workpiece using smaller width
pump points according to an embodiment.
[0021] FIGS. 4A-4C are diagrams showing top views of a rotating gas
flow in a 3-phase rotating crossflow plotted in time every
60.degree. according to an embodiment.
[0022] FIG. 5 illustrates a cross-sectional view of a portion of
wafer comprising a stacked memory device which may be processed by
a plasma treatment chamber with rotating gas crossflows according
to an embodiment.
[0023] Referring now to FIG. 6, a block diagram of a processing
tool is shown utilizing a machine learning (ML) model, in
accordance with an embodiment.
[0024] FIGS. 7A and 7B are flow diagrams illustrating a process for
generating a ML model, in accordance with an embodiment.
[0025] FIG. 8 shows a flow diagram illustrating a process for
developing a process recipe using a ML model is shown, in
accordance with an embodiment.
[0026] FIG. 9 shows a flow diagram illustrating a process for
baselining a processing tool, in accordance with an embodiment.
[0027] FIG. 10 illustrates a diagrammatic representation of a
machine in the exemplary form of a computer system within which a
set of instructions, for causing the machine to perform any one or
more of the methodologies described herein, may be executed
according to an embodiment.
DETAILED DESCRIPTION
[0028] The disclosed embodiments relate to a plasma chamber having
a rotating modulated cross-flow. In the following description,
numerous specific details are set forth, in order to provide a
thorough understanding of embodiments of the present disclosure. It
will be apparent to one skilled in the art that embodiments of the
present disclosure may be practiced without these specific details.
In other instances, well-known aspects, such as integrated circuit
fabrication, are not described in detail in order to not
unnecessarily obscure embodiments of the present disclosure.
Furthermore, it is to be understood that the various embodiments
shown in the Figures are illustrative representations and are not
necessarily drawn to scale.
[0029] Traditional plasma chambers (i.e., CCP or ICP) typically
inject gas axisymmetrically over a workpiece from gas inlet holes
that are typically located directly above the workpiece or
symmetrically around its periphery. As noted above, axisymmetric
gas flow can result in pressure and concentration gradients and the
gas hole inlets may breakdown, creating non-uniformities in the
workpiece. That is, as wear occurs in gas holes in the dense, high
|E| plasma regions, geometry of the holes change and as plasma
penetrates, the holes may modify the local plasma characteristics
in the vicinity of the holes. In addition, the local gas flow rate
and velocity may change as a result of geometric changes.
Therefore, the showerheads need to be replaced relatively often,
increasing cost of the workpiece.
[0030] Accordingly, embodiments disclosed herein are directed to a
plasma chamber (e.g., CCP or ICP) with a multiphase rotating
modulated gas cross-flow for etching, deposition or other materials
treatment. The plasma treatment chamber includes two or more gas
injectors and two or more pump ports along a sidewall. In a first
phase, one of the gas injectors forces a gas flow in one direction
generally parallel and across a surface of a workpiece or device,
where the gas is then pumped out via a pump port. In a second
phase, gas flow is rotated by using another gas injector to force
the gas flow in a different direction generally parallel and across
the surface of the workpiece, where the gas is then pumped out via
another pump port. In another embodiment, gas inlet valves coupled
to the gas injector and/or throttle valves coupled to the pump
ports can be used to modulate the rotating gas flows.
[0031] The plasma treatment chamber with rotating modulated gas
cross-flow eliminates the need for showerheads (and gas inlet
holes) in the dense, high |E| plasma regions, and therefore
prevents the source of plasma non-uniformity. The disclosed
embodiments prevent plasma from forming in gas holes due to
proximity to dense plasma or breakdown due to high electric fields,
leading to non-uniformity and plasma characteristics changing over
time change. The disclosed embodiments avoid high center-to-edge
pressure and concentration gradients that cause center-to-edge
processing differences. Pressure distribution can be tailored
across the plasma volume to minimize plasma non-uniformity. In
addition, the disclosed embodiments eliminate stagnant low-gas
velocity regions (i.e., center of the workpiece) for uniform
reactant and byproduct removal.
[0032] FIGS. 1A-1C are diagrams illustrating embodiments of a
plasma treatment chamber of a plasma reactor having a multiphase
rotating crossflow operation. FIG. 1A is a diagram illustrating a
top view of the plasma treatment chamber having a multiphase
rotating crossflow operation according to one embodiment. FIGS. 1B
and 1C illustrate cross-section views of the plasma treatment
chamber in different embodiments.
[0033] Referring to both FIGS. 1A and 1B, the plasma treatment
chamber 100A comprises one or more chamber sidewalls 112 with a
support surface 114 therein to hold a workpiece 116 (e.g., a
semiconductor wafer) for treatment. The plasma treatment chamber
100 may be used to perform a variety of treatments to the workpiece
116, such as etching, deposition, surface treatment or material
modification, by distributing gases inside the chamber. For
example, plasma treatment chamber 100A may comprise, but is not
limited to, a plasma etch chamber, a plasma enhanced chemical vapor
deposition chamber, a physical vapor deposition chamber, an ion
implantation chamber, an atomic layer deposition (ALD) chamber, an
atomic layer etch (ALE) chamber, or other suitable vacuum
processing chamber to fabricate various devices.
[0034] In one embodiment shown, the one or more sidewalls 112
surround a processing region 110 in which the workpiece 116 (e.g.,
wafer or substrate) is treated. In the example shown, the plasma
treatment chamber 100A is shown with an axially symmetrical shape
(e.g., a cylindrical) resulting in a single cylindrical sidewall
112. However, in other embodiments, the plasma treatment chamber
100A may have any other shape, such as an oval, which also results
in a single sidewall 112, or as a square or rectangle, in which
case the plasma treatment chamber 100A would have four
sidewalls.
[0035] According to the disclosed embodiments, the plasma treatment
chamber 100 includes at least two gas injectors 118A and 118B
(collectively referred to as gas injectors 118) and at least two
pump ports 120A and 120B (collectively referred to as pump ports
120) located generally along the sidewall(s) 112. In one
embodiment, the gas injectors are formed in the openings through a
liner of the sidewall 112. The plasma treatment chamber 100A may be
configured to use the gas injectors 118 and the pump ports 120 to
rotate gas flows 124 laterally across the workpiece 116 to provide
a multiphase rotating crossflow operation. In one embodiment, the
multiphase rotating crossflow operation comprises at least a
2-phase cycle, and may comprise a 3-phase cycle, a 4-phase cycle,
and so on, wherein each phase gas is injected from one side of
plasma treatment chamber 100A and pumped out generally from an
opposite side. As used herein, the phrase "located generally along
the sidewall(s)" is intended to describe that any of the gas
injectors 118 and/or pump ports 120 may be located in a sidewall or
horizontally abutting or adjacent to the sidewall, or located in an
outer periphery region of the chamber top or an outer periphery
region of the chamber bottom.
[0036] Rotation of gas flow laterally across the workpiece 116 may
result in improved control of gas velocity and pressure gradients
leading to better process uniformity across a wafer and from
wafer-to-wafer.
[0037] Referring to FIG. 1B, the plasma treatment chamber 100A
further includes a chamber lid 104 over the sidewall 112. A support
pedestal 108 may include a support surface 114 on which the
workpiece 116 is placed. In embodiments, the support pedestal 108
and the support surface 114 are fixed and not rotatable, and the
workpiece 116 affixed thereto is not rotated during processing. In
an embodiment, the workpiece 116 is electrostatically affixed to
the support surface 114. In another embodiment, the support surface
114 is moveable in the axial direction for plasma gap adjustment or
wafer transfer. A processing region 110 in the plasma treatment
chamber 100A is defined by an area between the chamber lid 104, the
support pedestal 108 (and support surface 114), and the sidewall
112. A chamber floor 106 is beneath the sidewall 112, and the
chamber floor 106 is below the processing region 110. The support
pedestal 108 is below the chamber lid 104 and above the chamber
floor 106, and is surrounded by the sidewall 112. In embodiments,
the chamber lid 104 and the support surface 114 may be separated by
distance of approximately 50 mm-90 mm. In an embodiment, the plasma
treatment chamber 100A is a parallel plate capacitively coupled
plasma (CCP) process chamber where a first electrode 105 is above
the workpiece 116. A second electrode is included in a location 113
in support pedestal 108 below support surface 114. In one
embodiment, the first electrode 105 is coupled to an RF source
having a frequency in a range of 40-200 MHz with a power in a range
of 200-10000 Watts. In one embodiment, the second electrode is
coupled to ground. A plasma is generated above the wafer and
between the two electrodes. In an embodiment, the workpiece 116 is
electrostatically clamped to the support surface 114 by one or more
clamping electrodes in or below the support surface 114. In
embodiments, the workpiece 116 is coupled to biasing electrodes
(e.g., at a low RF frequency in a range of 0.1 to 20 MHz) for
additional plasma control during processing. The generated plasma
may be pulsed during processing by pulsing the power to the first
electrode 105.
[0038] In an embodiment, the workpiece 116 may comprise any
substrate that is commonly used in semiconductor manufacturing
environments. For example, the workpiece may comprise a
semiconductor wafer. In an embodiment, semiconductor materials may
comprise, but are not limited to, silicon or III-V semiconductor
materials. The semiconductor wafer may be a
semiconductor-on-insulator (SOI) substrate in some embodiments.
Typically, semiconductor wafers have standard dimensions, (e.g.,
200 mm, 300 mm, 450 mm, or the like). However it is to be
appreciated that the workpiece 116 may have any dimension.
Embodiments may also include workpieces that comprise
non-semiconductor materials, such as glass or ceramic materials. In
an embodiment, the workpiece 116 may comprise circuitry or other
structures manufactured using semiconductor processing equipment.
In yet another embodiment, the workpiece 116 may comprise a reticle
or other lithography mask object.
[0039] FIGS. 1A and 1B illustrate an example of 2-phase cycle
rotating cross-flow operation. In the first phase, gas injector
118A injects a first gas flow 124A in a first direction generally
parallel to and across a surface of the workpiece 116 and has an
opposing pump port 120A along the one or more sidewalls 112
generally opposite of the gas injector 118A to pump out the gas
flow 124A. In the second phase, gas injector 118B injects a second
gas flow 124B in a second direction generally parallel to and
across a surface of the workpiece 116 and has an opposing pump port
120B along the one or more sidewalls 112 generally opposite of the
gas injector 118B to pump out the gas flow 124B. In embodiments,
the direction of the second gas flow 124B is different from the
direction of the first gas flow 124A. In one embodiment, generally
parallel means within approximately 0.degree. to 15.degree., and
generally opposite means within approximately 0.degree. to
30.degree..
[0040] Thus, gas injector 118A and the opposing pump port 120A form
one gas injector-pump port pair, while gas injector 118B and
opposing pump port 120B form a second gas injector-pump port pair.
In one embodiment, each of the gas injectors 118A and 118B may
comprise an array of individual gas injectors, as shown in FIG. 1A.
In an alternative embodiment, each of the gas injectors 118A and
118B includes only a single vent gas injector. In some embodiments,
gas injector 118A comprises an array of individual gas injectors,
and gas injector 118B is a single vent gas injector, or vice
versa.
[0041] As shown in FIG. 1A, along the horizontal plane, which is
generally parallel to the orientation of the workpiece 116, each
gas injector-pump port pair (i.e., a gas injector and the opposing
pump port) are symmetrically located along the sidewall 112 of the
plasma treatment chamber 100A. Any number of gas injectors 118 and
pump ports 120 may be provided. In general one gas injector-pump
port pair may be offset from an adjacent injector-pump port pair
locations by an angle equal to 360 total degrees divided by the
number of injector-pump port pairs to ensure equal distribution of
the gases. For example, with two injector-pump port pairs, the
injector-pump port pairs are offset from one another by 180.degree.
(360.degree./2). With three injector-pump port pairs, the
injector-pump port pairs are offset by 120.degree. (FIGS. 2A and
2B), and so on. In some embodiments, as shown, a gas injector span
is smaller than a span of the corresponding pump port. In other
embodiments, the gas injector span is the same as the span of the
corresponding pump port. In other embodiments, the gas injector
span is larger than the span of the corresponding pump port. Gas
can be injected from gas injector openings of various geometry such
as holes, slots, and the like, and different gas injectors can have
the same or different geometries and sizes.
[0042] While in some embodiments, the number of gas injectors 118
and pump ports 120 is equal, in other embodiments, the number of
gas injectors 118 and pump ports 120 may differ. In some
embodiments, a single pump port is associated with a corresponding
gas injector, as depicted. In other embodiments, an array of pump
ports is associated with a corresponding gas injector.
[0043] As shown in FIG. 1B, the gas injectors 118 are located in
openings in the sidewall 112 in the processing region 110. For
example, the openings may be located within a liner of the sidewall
112. In an embodiment, the openings in the sidewall 112 are in a
location vertically between the chamber lid 104 and the substrate
support pedestal 108. In the embodiment shown, the openings in the
sidewall 112 are adjacent to a bottom of the chamber lid 104.
[0044] Along the vertical plane, which is generally parallel to the
orientation of the support pedestal 108, locations of the pump
ports 120 may be vertically offset from locations of the gas
injectors 118 by a distance approximately equal to the distance
between a bottom of the chamber lid 104 and a top of the support
pedestal 108 in one embodiment. In this embodiment, the pump ports
120 may be located in cavities between the sidewall 112 and the
support pedestal 108, and above the chamber floor 106. In another
embodiment, the pump ports 120 may be located in additional
openings in the sidewall 112 anywhere between the chamber lid 104
and the chamber floor 106. In another embodiment, gas can be
injected from peripheral regions of the chamber top, and/or pumped
from peripheral regions of the chamber bottom, and over the
workpiece processing region and still flow substantially parallel
to the workpiece.
[0045] As described above, the plasma treatment chamber 100A of the
disclosed embodiments injects gas generally parallel and across the
workpiece 116. This is in contrast to a typical axisymmetric
top-down gas flow injection from a "showerhead" electrode in a CCP
source reactor, and in contrast to a radial outward/downward gas
injection from a nozzle array near a central axis in an ICP or
microwave source reactor. In addition, instead of a pump port or
pumping plenum located axisymmetrically around the periphery of the
workpiece, in embodiments, gas is preferentially pumped out from a
side of a workpiece generally opposite the injection side.
[0046] In embodiments, the gas flow 124 of each cross flow phase
can be switched on and off to control gas flow rotation. In another
embodiment, instead of switching the gas flow 124 on and off, a
modulating function may be applied to a flow rate of the gas flows
124 from the gas injectors 118 and/or to an outlet conductance (or
pressure) caused by the pump ports 120 to either approximate
open/closed states or to ramp between states using a modulating
function, such as sinusoidal. As shown in FIG. 1B, a flow rate of
one or both of the first and second gas flows 124A and 124B can be
modulated using one or more gas inlet valves 122A and 122B (e.g.,
piezoelectric valves) that are coupled to gas injector 118A and
118B, respectively. In embodiments, the gas inlet valves 122A and
122B are coupled to one or more gas sources 126, such that a single
type of gas, or a mixture of different types of gases, may be
injected into the processing region 110 during each rotation phase.
In one embodiment, a constant total gas flow may be applied by the
gas injectors 118 to smoothly and sequentially inject the gas flows
across the different sides of the workpiece 116 in a complete
cycle, which may then be repeated as necessary.
[0047] In addition, in some embodiments one or more of the pump
ports 120 may be modulated. For example, pump port conductance
(pressure) may be modulated using individual pressure control
valves 127A and 127B on pump ports 120A and 120B. Also shown is
that the pump ports 120A and 120B are coupled to one or more pumps
132 to evacuate the gas. In the example shown, pressure control
valve 127A in pump port 120A is in the closed position, while
pressure control valve 127B is shown in the open position to expel
the first gas flow 124A. The pressure control valves 127A and 127B
may be operated smoothly between two states of conductance or
pressure, which are then cycled through in a like sequence as the
gas injectors 118A and 118B. In one embodiment, pressure control
valves 127A and 127B comprise throttle valves.
[0048] The plasma chamber 100A may inject a variety of types of
process gases. Exemplary process gases may include the following:
i) dielectric etch gases including one or more of C4F8, C4F6, C3F6,
CH2F2, C3H2F4; ii) deposition gases including one or more of CH4,
C2H2; iii) additional gases for co-flow for either etch or
deposition including one or more of Ar, N2, O2, He, Kr, Xe, COS;
iv) semiconductor material etch deposition gases including one or
more of SiCl4, SiCH2Cl2; v) hydride-based deposition gases
including one or more of BH3, AlH3, GaH3, NH3; vi) oxide material
etch deposition gases including one or more of SiCl4, SiCH2Cl2, and
O2; and vii) annealing gases including one or more of NH3, N2,
Ar.
[0049] In some embodiments, the plasma treatment chamber 100A may
further include sensors 131 and systems to monitor process chamber
conditions including gas flow, velocity, pressure, temperature and
the like, with high sensitivities and real time measurement.
Particular embodiments can include capacitive wall sensors, on-chip
or off-chip thermal sensors, pressure sensors, and/or integrated
sensors (capacitive sensors and thermal sensors) on substrates such
as ceramic substrate or glass or silicon or flexible substrates. In
some embodiments, the sensors can be distributed throughout the
chamber to monitor the chamber conditions at various locations,
which then can be correlated to overall process performances such
as etch rate, etch non-uniformity, particle generation, process
drifting, pressure uniformity, etc. In one embodiment, a plurality
or an array of pressure sensors can be distributed throughout the
chamber to provide data regarding gas flow (e.g., rotation rates,
uniformity, velocity) during processing.
[0050] FIG. 1B further shows that the plasma treatment chamber 100A
may be connected to a controller 140, which in turn may be
connected to a user interface 142. In some embodiments, the
controller may be coupled to the gas inlet valves 122, the pressure
control valves 127, the gas sources 126, the pump 132 and the
sensors 131 to control operation of the plasma treatment chamber
100A. A user may set process parameters and monitor operation of
the plasma treatment chamber 100A through the controller 140 from
the user interface 142.
[0051] The multiphase architecture of the plasma treatment chamber
enables many different configuration options. For example, FIG. 1C
illustrates a cross-section view of the plasma treatment chamber
100B in an embodiment that includes a top-down gas flow in addition
the one or more pairs of gas injectors 118 and pump ports 120 that
provide side-to-side gas flows. In this embodiment, chamber lid 104
may be configured with a showerhead plate 128 (the controller and
UI of FIG. 1B are not shown for simplicity). The shower head plate
128 may have a central manifold 129 and one or more outer manifolds
130 for distributing gases into the processing region 110 along
with gases distributed by the gas injectors 118A and 118B. Using
the showerhead plate 128, additional gases may be introduced into
the chamber with a vertical velocity component, but injection of
gasses from one side by gas injector 118A and pumping out on other
side of workpiece 116 by pump port 120B generally results in a
horizontal component of gas velocity across much of the workpiece
116. Likewise, while the pump ports 120 may be on the sidewall 112,
or on an upper or lower surface of chamber, the pump ports 120 are
generally across from the injection side. Therefore, while there
may be a component of velocity of exiting gas in the vertical
direction, gas velocity is generally horizontal and parallel to the
workpiece 116 in the region above workpiece 116.
[0052] FIGS. 2A-2C are diagrams illustrating a plasma treatment
chamber of a plasma reactor having a 3-phase rotating crossflow
operation according to one embodiment. FIG. 2A is a schematic of an
angled semi-transparent view of the 3-phase rotating crossflow
plasma treatment chamber. FIG. 2B is a schematic of a top view of
the 3-phase rotating crossflow plasma treatment chamber according
to another embodiment.
[0053] Referring to FIG. 2A, the plasma treatment chamber 200A
having a 3-phase rotating crossflow operation is similar to the
embodiment shown with respect to FIGS. 1A-1C in that the chamber
200 includes a sidewall 212 surrounding a workpiece 216. However,
in addition to two gas injectors 218A and 218B and two opposing
pump ports 220A and 220B, the plasma treatment chamber 200 further
includes gas injector 218C and opposing pump port 220C located on a
generally opposite side of the sidewall 212 to pump out the gas
flow. Gas injector 218A and the opposing pump port 220A form one
gas injector-pump port pair, gas injector 218B and opposing pump
port 220B form a second gas injector-pump port pair, and gas
injector 218C and opposing pump port 220C form a third gas
injector-pump port pair. (Gas injectors 218A-218C are collectively
referred to as gas injectors 218, and pump ports 220A-220C are
collectively referred to as pump ports 220.)
[0054] In this embodiment, the gas injectors 218 each comprise as a
single vent in the sidewall 212, as shown. In one embodiment, the
gas injectors 218 are symmetrically arranged about the central axis
of the plasma treatment chamber 200, and the pump ports 220 are
symmetrically arranged about the central axis of the plasma
treatment chamber 200, as shown. In the 3-phase rotating cross flow
embodiment comprising three injector-pump port pairs, the
injector-pump port pairs are offset from one another by 120.degree.
(360.degree./3). More specifically, the gas injectors 218 are
located approximately 120.degree. from one another, and the pump
ports 220 are located 120.degree. from one another. The pump ports
220 dispersed laterally between the spaced-apart gas injectors 218
as well as vertically offset from the gas injectors 218.
[0055] FIG. 2B shows a top view of plasma treatment chamber 200B
comprising an array of individual gas injectors, referred to as gas
injector array 218D, where the individual gas injectors are
distributed about a periphery of the sidewall 212. Also shown are
three gas inlet valves 122A-122C, and three pressure control valves
127A-127C, one per pump port 120 (see FIG. 1B). Sets of the smaller
gas injectors in the gas injector array 218 (such as four
injectors, as shown) may be modulated by a single one of the gas
inlet valves 122A-122C to create gas flows in various directions
across the workpiece 216. The gas flow is then pumped out by one of
the pump ports controlled by a corresponding one of the pressure
control valves 127A-127C generally opposite from the modulating gas
inlet valves 122A-122C. In this case, in an embodiment, the gas
injector span is larger than the span of the corresponding pump
port, resulting in a somewhat converging flow (e.g., flow 299) to a
relatively narrower pump port.
[0056] FIG. 2C illustrates a timing diagram for the 3-phase
rotating crossflow operation performed by plasma treatment chamber
200B in further detail. The timing diagram assumes the presence of
three gas inlet valves 122 (GV1, GV2, GV3), and the presence of
three pressure control valves 127 (PV1, PV2, PV3). The X-axis of
represents time and the Y-axis represents i) a percentage of gas
valve open in the bottom row, a percentage of pump port closed in
the middle row, and chamber pressure as measured by a Baratron
(manometer) in the top row.
[0057] A controller may be coupled to the plasma treatment chamber
200 and configured to control the gas inlet valves 122A-122C and
pressure control valves 127A-127C. The controller starts the first
phase by fully opening GV1 to 100 percent, and partially opening
GV2 and GV3, for example, at approximately 2-5%. During the first
phase, PV1 is opened while PV2 and PV3 are closed, and chamber
pressure is between 1 mT and 500 mT.
[0058] GV1 begins closing near a transition between the first phase
and the second phase, and the direction of the gas flow is rotated
by fully opening GV2 to 100 percent to begin the second phase. GV1
and GV3 are partially open at approximately 2-5%. During the second
phase, the controller opens PV2 and keeps PV1 and PV3 closed.
Chamber pressure may remain between 1 mT and 500 mT in some
embodiments, or between 10 mT and 200 mT in other embodiments.
[0059] Near a transition between the second phase and the third
phase, GV2 is ramped down, and the direction of the gas flow is
rotated by opening GV3 to 100 percent to begin the third phase. GV1
and GV2 are partially open at approximately 2-5%. During the third
phase, the controller opens PV3 and keeps PV1 and PV2 closed. This
completes the 3-phase cycle, which may be repeated as necessary. As
shown, a relatively constant chamber pressure is maintained during
the three gas flow phases. In an embodiment, opening and closing
GV1, GV2 and GV3 sequentially effectively creates a rotational gas
flow, which may mimic rotation of a wafer. In one embodiment, a
single full rotation of the gas flow is performed at a rate
approximately in a range of 100 ms to 10 sec.
[0060] Many different variations between the gas flow phases and
cycles may occur. That is each parameter controlling operation of
the plasma treatment chamber may vary across phases and cycles. For
example, the time to complete a full cycle may be the same or
different across different cycles. The time to complete a phase may
be the same or different within a cycle, and may be the same or
different across different cycles. The direction of gas flow
rotation (e.g., clockwise, counterclockwise) may be the same or
different within phases of a cycle, may be non-sequential, or may
be the same or different across cycles. The velocity of the gas
flows may be the same or different within phases of a cycle, or may
be the same or different across cycles. The % open of the gas
valves and the time the gas valves are open may be the same or
different within phases of a cycle, or may be the same or different
across cycles. The % open of the pressure control valves and the
time the pressure control valves are open may be the same or
different within phases of a cycle, or may be the same or different
across cycles. For example, in an embodiment, rotation is performed
for a first portion of a process at one rate, and is then slowed to
a second rate for a second portion of the process. In an
embodiment, rotation is performed for a first portion of a process
at one rate, and is then sped up to a second rate for a second
portion of the process. In an embodiment, rotation is fast for a
first portion of a single rotation cycle, and slows for a second
portion of the rotation. In an embodiment, rotation is slow for a
first portion of a single rotation cycle, and is sped up for a
second portion of the rotation. By varying rotation speed within a
single cycle, or cycle to cycle, process non-uniformities may be
compensated for. In other embodiments, direction is changed between
clockwise and counter-clockwise within a cycle, cycle-to-cycle, or
between sets of cycles. Likewise, in embodiments, gas flow rates
between a first phase, a second phase, and a third phase can be
varied within a cycle, cycle-to-cycle, or between sets of
cycles.
[0061] FIGS. 2D illustrates an angled view of a top of the chamber
lid 104 showing a gas delivery system there above. In one
embodiment, the gas delivery system 225 comprises an array of gas
inlet valves 122, where each of the gas inlet valves 122 are
located above, and symmetrically arranged, around a perimeter of
the chamber lid 104. In the embodiment shown, gas delivery system
225 comprises 6 gas inlet valves 122, but the specific number may
vary, e.g., two or more. A top side of each of the gas inlet valves
122 may be connected to a gas line assembly 250 arranged in a spoke
and hub formation, where the hub is connected to the gas sources
126 shown in FIGS. 1B and 1C. A bottom side of the gas inlet valves
122 may be connected to respective sets of recursive gas lines 252.
Each set of recursive gas lines 252 may be coupled to one or more
gas injectors 118. In the specific embodiment shown, there are 6
sets of recursive gas lines 252 with 4 inlets each coupled to the
gas injectors 118 for a total of 24 inlets.
[0062] In embodiments, the gas inlet valves 122 may comprise analog
variable conductance fast gas valves that allow fast response
without excessive pressure spikes that lead to gas light up or
arcing or make it difficult for RF match control to follow.
Specific examples of the gas inlet valves include commercially
available Swagelok eDE Valves and Fujikin Piezo Valves. The
Swagelok eDE Valves may have 15-20 msec open/close times, are good
for sealing atm/vacuum, and have a lifespan of 40M cycles. The
Fujikin Piezo Valves have a proportional flow, a 10 msec open/close
time and may have a lifespan much greater than 40M cycles depending
on use. Both may provide gas flows up to 2.5 slm @400 T upstream
pressure.
[0063] FIG. 2E illustrates an angled cross-section view of the
plasma chamber. This view shows the connections between the
recursive gas lines 252 and gas injectors 118. Also shown is that
one embodiment, sidewall 112 may comprise an outer sidewall 112A
and an inner sidewall 112B (or liner), and the gas injectors are
formed in a space between the outer sidewall 112A and the inner
sidewall 112B, and the gas is injected from the recursive gas lines
252 through openings in the inner sidewall 112B.
[0064] FIGS. 2F-2H illustrate angled and cross-section views of a
vacuum chamber in which the pump ports 120 are formed. In
embodiments, the vacuum chamber 275 is under dynamic vacuum
controlled by pump 132 (FIGS. 1B and 1C). In one embodiment, the
vacuum pressure may range from 1 mT to 500 mT. In one embodiment,
the chamber floor 106 comprises an upper chamber floor 106A and a
lower chamber floor 106B, and the pump ports 120 are formed within
cavities in the vacuum chamber 275 between the upper chamber floor
106A and the lower chamber floor 106B. The pump ports 120 are also
shown symmetrically arranged around the support pedestal 108.
[0065] Actuators 277 are coupled to the pressure control valves 127
to control each of the pump ports 120. FIG. 2H shows that the pump
ports 120 are closed and opened by one of the actuators 277 raising
and lowering a corresponding pressure control valve 229 within the
cavity of each pump port 120. FIG. 2F shows that in one embodiment,
the pressure control valves 229 may comprise a single unitary body
to seal the associated port, while FIG. 2G shows that in another
embodiment, pressure control valves 229 may divided into one or
more adjacent sections (2 in this case), each controlled by a
corresponding actuator 277. In an embodiment, referring to FIG. 2H,
pressure control valve 127 on the left is down (OPEN), and pressure
control valve 127 on the right is up (CLOSED). In FIGS. 2F and 2G,
all pressure control valves are shown in a CLOSED position.
[0066] FIGS. 2I-2K are diagrams illustrating an angled
semi-transparent view of an example inductively coupled plasma
(ICP) chamber having a 3-phase rotating crossflow according to one
embodiment. As shown in FIG. 2I, the ICP chamber 280 includes an
electrode 282 in the form of a planar multi-spiral coil adjacent to
the chamber lid (not shown). The electrode 282 includes a post 286
that is RF driven and may include three grounded ends 284 along the
largest radii. FIG. 2J shows gas injectors 288 located
symmetrically arranged around an outer periphery of the chamber
top. In one embodiment, the gas injectors 288 may comprise
60.degree. wide inlets with a 60.degree. wide space therebetween.
FIG. 2K shows pump ports 290 symmetrically arranged around an outer
periphery of the chamber bottom, each located directly 180.degree.
opposing one of the gas injectors 288.
[0067] FIGS. 3A-3F are diagrams illustrating top views of a plasma
treatment chamber having a 4-phase rotating crossflow operation
according to one embodiment. FIG. 3A is a diagram illustrating the
plasma treatment chamber 300, which may have a square shape having
four sidewalls 312. Each of the four sidewalls 312 includes one of
the four gas injector arrays 318A-318D and one of the four opposing
pump ports 320A-320D.
[0068] FIG. 3B is a diagram illustrating a 4-phase rotating
crossflow operation. Over a 4-phase cycle, gas is injected from
each of the four sidewalls 312 and pumped out from an opposite
side. Conductance of each pump ports 320A-320D can be modulated
with fast individual throttle valves. Phase 1 shows a left to right
first gas flow. Phase 2 shows a clock-wise rotation to a
top-to-bottom second gas flow. Phase 3 shows a clock-wise rotation
to a right-to-left third gas flow. And phase 4 shows a clock-wise
rotation to a bottom-to-top fourth gas flow. In one embodiment,
each phase may last approximately 0.5 to 2 seconds depending on the
application.
[0069] FIGS. 3C and 3D are diagram illustrating a 4-phase rotating
crossflow operation with deliberate non-uniform center and edge gas
injection with opposite side port pumping according to a further
aspect of the disclosed embodiments. In this embodiment, the
individual gas injectors in each of the gas injector arrays
318A-318D can be switched on/off or have a modulated flow rate
controlled by the gas inlet valves 122. FIG. 3C shows a 4-phase
example of a center-to-edge gas flow, where in each phase the gas
flow injected from center ones of the individual gas injectors in
each of the gas injector arrays 318A-318D has a greater flow rate
relative to edge ones in the gas injector arrays 318A-318D. FIG. 3D
shows a 4-phase example of an edge-to-center gas flow, where the
gas flow injected from edge ones of the individual gas injectors in
each of the gas injector arrays 318A-318D has a greater flow rate
relative to center ones in the gas injector arrays 318A-318D. Such
non-uniform center and edge gas injection of the disclosed
embodiments may be deliberately changed and controlled over time to
control workpiece process uniformity. In an embodiment, during one
cycle, between cycles, or between sets of cycles, relative center
and edge flows of one or more of the gas injectors are varied.
[0070] FIG. 3E is a diagram illustrating a single phase of a
multiphase (e.g., 4-phase) rotating crossflow operation in which at
least a portion of the gas flow is diverted to the sides of the
workpiece rather than a 100% cross-flow across the workpiece. In
this extreme case, the opposing pump port is closed while the side
pump ports are open, minimizing gas flow and velocity across the
center of the workpiece. This process may be used to control
uniformity. In embodiments, such a diverted gas flow as shown in
FIG. 3E is used for an entirety of a process, or for only a portion
of a cycle or for one or a smaller set of cycles in a process
scheme. In embodiments, a diverted gas flow is rotated around a
chamber for one or many cycles.
[0071] FIG. 3F is a diagram a single phase of a multiphase cycle
where gas flow is directed across the workpiece using smaller width
pump points. As in FIG. 3C, the gas flow from center ones of the
individual gas injectors in each gas injector array has a greater
flow rate relative to edge ones in the gas injector array, and the
opposing pump port is open, while the others are closed. In a
further embodiment, the smaller width pump ports compared to the
embodiments described above, force the gas flow across the center
region of the workpiece. In this embodiment, for a typical 300 mm
wafer chamber, the smaller pump ports may have dimensions of 3.5''
wide.times.(1/plurality).times.(14'') long center line radial arc
length, while the larger single pump ports may have dimensions of
3.5'' wide.times.14'' long center line radial arc length. In
general, the pump ports should have dimensions, or size, adequate
for the process applications flow conductance while narrow enough
port width opening to promote uniform "cross flow" over the wafer
from gas inlet side of chamber to pump port side.
[0072] FIGS. 4A-4C are diagrams showing top views of a rotating gas
flow in a 3-phase rotating crossflow plotted in time every
60.degree.. The arrows represent vectors showing a magnitude of
velocities and the contours represent pressure gradients. Snapshots
of the gas flow are shown at 0.degree., 60.degree., 120.degree.,
180.degree., 240.degree., and 300.degree.. A graph in FIG. 4C shows
that gas injector and pump port pressures over time are relatively
consistent across the 3-phases.
[0073] The example operations shown in FIGS. 4A-4C may be used
individually or more likely in combination, over a repetitive
cycle, for maximizing process uniformity. This tuning capability,
which uses gas injection and/or pumping at peripheral boundaries
and outside of the dense plasma region as control inputs, without
introducing geometric discontinuities (i.e., gas injection holes),
allows formation of a uniform plasma with minimal drift or change
over time due to etching, wear, or coating of exposed plasma facing
surfaces, namely electrode/showerheads with gas holes or gas
nozzles. The use of rotating modulated cross-flows can permit
process uniformity control from the peripheral boundaries of the
chamber.
Reactive Ion Etching
[0074] As an example application, the plasma treatment chamber may
be used to perform precise reactive ion etching during
semiconductor manufacturing.
[0075] FIG. 5 illustrates a cross-sectional view of a portion of
wafer comprising a stacked memory device as processed by the plasma
treatment chamber with rotating gas crossflows according to one
embodiment. In one embodiment, an intermediate structure of the
stacked memory device is shown during fabrication. In one
embodiment the intermediate structure 400 will comprise a 3D-NAND
structure and includes a substrate 402, an alternating layer stack
404 over the substrate 402, inter layer dielectric (ILD) layers 406
over the alternating layer stack 404, and a mask layer 408 over the
ILD layers 406. The alternating layer stack 404 may comprise
interleaved insulator layers 404A and 404B (e.g., silicon nitride,
silicon oxide and the like). Examples of the ILD layers 406 may
include spin-on-glass (SOG), SOC, and SiON.
[0076] The mask layer 408 may define the pattern of an integrated
circuit, with a pattern to guide deposition or removal of material
from the wafer in subsequent patterning steps. In this example,
reactive ion etching is performed by the plasma treatment chamber
to remove the material between some of the openings in the mask
layer 408 to form openings 410 through the ILD layers 406 and the
alternating layer stack 404 to the substrate 402, where the
intersections of the openings 410 and the metal layers 404A may
eventually form a memory cell. The gas flows injected by plasma
treatment chamber (as described above) can be customized to control
both etch depth uniformity as well as aspect ratio (depth-to-width)
uniformity of the openings 410. In one embodiment, one or more the
openings 410 may be etched to have a first aspect ratio through the
ILD layers 406 and a second aspect ratio through the alternating
layer stack 404. In embodiments, one or more of the openings 410
may have a varying aspect ratio, referred to as bowing, through the
alternating layer stack 404, as shown. In one embodiment, the
openings 410 may be etched to have high aspect ratios greater than
8-1, 9-1 or 10-1. In embodiments, one or more the openings 410 may
also have varying etch depth.
[0077] In embodiments, 3D-NAND ion etch applications may include a
pillar etch as described above, a slit etch, a peri contact etch, a
staircase contact etch, a cell contact-1 etch, and a cell contact-1
etch. In embodiments, aspect ratios, etch depths and bowing
characteristics may be parameters that are monitored by a machine
learning model, as described below.
Use of a Machine Learning (ML) Model to Control a Plasma Treatment
Chamber Having a Multiphase Rotating Crossflow
[0078] Configuring the plasma treatment chamber described above to
provide a desired outcome on a workpiece (e.g., wafer) requires a
process recipe that comprises a complex combination of many
different processing parameters (i.e., knobs) that can be
individually controlled. Examples include total gas flow mixture,
gas pressure (mTorr), gas flow ramp open times (msec), gas flow
time (msec), gas flow ramp closed times (msec) and the like.
[0079] In order to develop a process recipe for high volume
manufacturing (HVM) process engineers rely on their experience and
expertise to identify a baseline recipe that may provide a rough
approximation of the desired outcome on the wafer. A design of
experiment (DoE) that relies on the processing of a set of wafers
(or coupons) in order to identify how the knobs interact is then
generated around the baseline recipe. The DoE results may be
interpreted by the process engineer to further refine the baseline
recipe. Additional DoEs may also be executed in order to converge
on the desired outcome on the wafer. Such an iterative process is
time and resource intensive.
[0080] Additionally, once the final processing recipe has been
developed, chamber drift during many iterations of the process for
different wafers may result in changes to the outcome on the wafer.
Chamber drift may be the result of erosion of consumable portions
of the chamber, degradation of components (e.g., sensors, lamps,
etc.), deposition of byproduct films over surfaces, or the like.
Accordingly, additional tuning is needed even after the extensive
recipe development process.
[0081] Consequently, recipe development and chamber baselining are
time and resource intensive. Particularly, the process space
available to tune and optimize a given process is extremely large,
and it is practically impossible to explore the entire process
space empirically within any reasonable timeframe. Furthermore, due
to the interaction between processing parameters and their impact
on the process performance, it is extremely hard to predict the
combined effect of simultaneous variation of multiple processing
parameters by manually scanning one processing parameter at a
time.
[0082] A second aspect of the disclosed embodiments comprises a
semiconductor manufacturing tool utilizing one or more machine
learning (ML) models to control the plasma treatment chamber having
a multiphase rotating cross-flow. The ML model may be used for
developing process recipes and/or processing a device or workpiece.
The ML model may connect input processing parameters to device
outputs.
[0083] In an embodiment a method of controlling processing
comprises querying the ML models to control timing of the gas flow
rotation. In an embodiment, a method for developing a semiconductor
manufacturing process recipe comprises selecting one or more device
outcomes, and querying the ML model to obtain a process recipe
recommendation suitable for obtaining the device outcomes when
processed by the plasma treatment chamber having a multiphase
rotating cross-flow. This may be referred to as feed forward
process adjustment. In an embodiment, the method may further
comprise executing a design of experiment (DoE) on a set of wafers
to validate the process recipe recommended by the ML model.
Measurements of the DoE may be taken and used to change the process
recipe for future wafers, for feedback process adjustments.
[0084] Additionally, the ML model may be updated during processing
of wafers in a chamber as on-tool performance becomes available and
then update a process recommendation or actively change the recipe.
This may be referred to as "on the fly" or real-time process
adjustments.
[0085] Recipe changes may include modifying the recipe within a
step, e.g., increasing the rotation frequency of the gas flows when
etching the top of the wafer and lowering the rotation frequency as
it reaches lower, or vice versa. Another example is the updated
machine learning model modifying inputs parameters within a single
rotation, such as making the etch depth slightly different at the
beginning and the end of a gas flow rotation when processing of the
stacked memory device of FIG. 5. The updated ML model can provide
accurate tracking of chamber drift and allows for revisions to the
process recipe to be made without extensive DoE of physical wafers
or reliance on only the experience and knowledge of a process
engineer.
[0086] Accordingly, embodiments disclosed herein leverage the use
of a ML model to query an entire process space without the need to
process physical wafers in a large design of experiment (DoE).
Therefore, time and resources dedicated to recipe development can
be significantly reduced.
[0087] The ML model may be a model of a process space generated
from the combination of a statistical model and a physical model.
As used herein, a "process space" may refer to a multi-dimensional
process space that maps processing parameters to one or more device
outcomes on the wafer. The processing parameters, sometime called
knobs, are variables that can be controlled to control a process.
For example, knobs or processing parameters may include, but are
not limited to, any combination of: temperature, RF source power,
bias power, gas pressure (mTorr), gas flow ramp open times (msec),
gas flow time (msec), gas flow ramp closed time (msec), gas flow
fraction at various gas injectors, gas composition at various
injectors, gas flow fraction going to various injectors, gas flow
rotation frequency, gas flow composition frequency, gas flow
rate/velocity (pressure gradient), gas flow direction, gas rotation
phase, electron/plasma density, plasma density gradient, electron
temperature, ion current density, plasma potential, sheath electric
field, potential, sheath electric field tilt angle, sheath electric
field z-component, mass fractions,fluxes, and ion current density
to workpiece.
[0088] The device outcomes may refer to measurable properties of
features on a wafer after processing. For example, the selected
device outcomes may comprise any combination of: a feature profile,
a layer thickness, a thickness uniformity, a material composition
of a layer, a composition uniformity, a porosity, a film stress,
process uniformity across chambers in a facility (e.g., chamber
matching), wafer to wafer uniformity, uniformity between different
wafer lots, and the like. During an etch processes, the selected
device outcomes may further include any combination of: etch rate,
etch or uniformity center-to-edge, etch rate uniformity azimuthal,
etch feature uniformity (generally described by top v. bottom
critical dimension (CD)), tilt, bow, and mask remaining, and the
like. That is, device outcomes are not limited to an outcome on a
single wafer. Each point in the process space may be a
representation of a set of processing parameter values and the
resulting device outcome (or outcomes) produced by the set of
processing parameters.
[0089] In an embodiment, the statistical model of the ML model may
be built using a DoE of actual wafers to populate a portion of the
process space. Algorithms may then be used to extrapolate the
remainder of the process space. The physical model is based on real
world physical and chemical interactions that occur within the
processing chamber. A simulation of the physical and chemical
interactions in the processing chamber over a range of different
processing parameters may be used to generate the physical model.
In an embodiment, the physical model is merged with the statistical
model to provide the ML model. For example, the physical model may
be used to fill any gaps in the statistical model and/or to verify
extrapolated data points.
[0090] Referring now to FIG. 6, a block diagram of a processing
tool 600 is shown utilizing a ML model, in accordance with an
embodiment. The processing tool 600 comprises tool hardware 640
corresponding to the plasma treatment chamber described above, a
machine learning model server 620, a front end server 660, and a
control server 650.
[0091] In an embodiment, the ML model server 620 may include a
statistical model 625 and a physical model 627. The statistical
model 625 and the physical model 627 may be communicatively coupled
to a database 630 for storing input data (e.g., sensor data, model
data, metrology data, etc.) used to build and/or update the
statistical model 625 and the physical model 627.
[0092] In an embodiment, the statistical model 625 may be generated
from a physical DoE and use interpolation to provide an expanded
process space model. The physical wafers that are processed may be
used to provide a mapping of processing parameters to specific
device outcomes. The physical DoE may also be used to identify
interactions between different processing parameters. After the
data (e.g., metrology data, sensor data, process parameter data,
etc.) for the physical wafers is provided, interpolation is used to
fill gaps in the process space. In an embodiment, data, such as
metrology data, may be obtained using an external tool that is
communicatively coupled to the ML model server 620 by a data link
(e.g., a wired or wireless data link).The interpolation may be done
using any suitable algorithm or algorithms. Algorithms may include,
but are not limited to a neural network, deep learning or any other
known techniques used for regression analysis (e.g., linear,
partial least squares, Gaussian, polynomials, convolution neural
networks for regression, regression trees and others).
[0093] In an embodiment, the statistical model 625 may be provided
as a module that is sold or licensed for use in conjunction with
the processing tool. That is, a physical DoE for the statistical
model 625 may be executed by the manufacturer of the processing
tool. In other embodiments, the statistical model 625 may be
generated by executing the physical DoE on-site. In yet another
embodiment, a generic statistical model 625 may be provided by the
tool manufacturer and a subsequent physical DoE may be executed
on-site to provide a calibration of the statistical model 625 to
more closely model the particular processing tool being
investigated.
[0094] In an embodiment, the physical model 627 may be generated
using real world physics and chemistry relationships. For example,
physics and chemistry equations for various interactions within a
processing chamber may be used to build the physical model. The
physical model 627 may also utilize chamber geometries or other
chamber configurations to improve the accuracy of the physical
model 627. The physical model 627 may be the result of a simulation
of the physical and chemical interactions within a processing tool
across a plurality of different processing parameters. The physical
model 627 may be a module that is sold or licensed for use in
conjunction with the processing tool.
[0095] In an embodiment, the physical model 627 and the statistical
model 625 may be able to reference each other (as indicated by the
arrow). Cross-referencing between the two models 627 and 625 allows
for validation of each of the models and for filling in any gaps in
the individual models. In an embodiment, the physical model 627 and
the statistical model 625 may be combined to provide a more robust
ML model.
[0096] As shown, the ML model server 620 may be integrated with the
processing tool 600. For example, the ML model server 620 may be
communicatively coupled to a front end server 660 by a network
connection, as indicated by the arrow. However, in other
embodiments, the ML model server 620 may be external to the
processing tool 600. For example, ML model server 620 may be
communicatively coupled to the processing tool 600 through an
external network or the like.
[0097] In an embodiment, the front end server 660 may comprise a
user interface 665 for the ML model server 620. The user interface
665 provides an interface for a process engineer to utilize the ML
modeling in order to execute various operations, such as recipe
development or chamber baselining, as will be described in greater
detail below. In one embodiment, the user interface 665 may
correspond to user interface 142 of FIG. 1B.
[0098] The control server 650 may comprise a smart monitoring and
control block 655. The smart monitoring and control block 655 may
comprise modules for providing diagnostics and other monitoring of
the processing tool 600. Modules may include, but are not limited
to health checks, sensor drift, fault recovery, and leak detection.
The smart monitoring and control block 655 may receive data from
various sensors implemented in the tool hardware 640 as inputs. The
sensors may include standard sensors 647 that are generally present
in semiconductor manufacturing tools 600 to allow for operation of
the tool 600. The sensors may also include modelling sensors 645
that are added into the tool 600. The modelling sensors 645 provide
additional information that is necessary for the building of highly
detailed ML models. For example, the modelling sensors may include
virtual sensors and/or witness sensors. Virtual sensors may utilize
the data obtained from two or more physical sensors and implement
interpolation and/or extrapolation in order to provide additional
sensor data not obtainable from physical sensors alone. In a
particular example, a virtual sensor may utilize an upstream
pressure sensor and a downstream pressure sensor in order to
calculate a flow rate through a portion of the processing tool,
such as a gas cartridge. Generally, modelling sensors may include
any type of sensor, such as, but not limited to, pressure sensors,
temperature sensors, and gas concentration sensors. In an
embodiment, the smart monitoring and control block 655 may provide
data that is used by the ML model server 620. In other embodiments,
output data from the various modelling sensors 645 may be provided
directly to the ML model server 620. In one embodiment, the control
server 650 may correspond to controller 140 of FIG. 1B.
[0099] Referring now to FIG. 7A, a flow diagram illustrating a
process for generating a ML model is shown, in accordance with an
embodiment. In an embodiment, input from a modeling DoE 715 is
inputted into a statistical model engine 724. The modeling DoE 715
may include the processing of a number of physical wafers. The DoE
715 may include various data sources that are fed to the
statistical model engine 724. For example, metrology data 716
obtained during or after processing the wafers may be provided to
the statistical model engine 724. Additionally, sensor data 217
from sensors in the processing tool may be provided to the
statistical model engine 724. Process parameter data 718 (i.e., the
values of various process parameters during the processing of the
wafers) may also be provided to the statistical model engine
724.
[0100] In an embodiment, the statistical model engine 724 may be
implemented as hardware and/or software suitable for analyzing the
various data sources and outputting a statistical model 725. The
statistical model engine 724 may utilize machine learning based on
neural networks, or any other known techniques used for regression
analysis (e.g., linear, partial least squares, Gaussian,
polynomials, convolution neural networks for regression, regression
tress, and others) in order to interpolate a larger process space
than is available from the physical DoE data alone.
[0101] In an embodiment, a physical model engine 726 is used to
generate the physical model 727. In an embodiment, the physical
model engine 726 may be implemented as hardware and/or software.
The physical model engine 726 takes as inputs the chamber
configuration and real world physics and chemical equations. The
physical model engine 726 may implement a simulation of the
physical and chemical interactions within a processing tool across
a plurality of different processing parameters in order to build
the physical model 727. As such, changes to processing parameters
that modify the physical and/or chemical reactions in the
processing tool may be mapped to expected device outcomes.
[0102] In an embodiment, the statistical model 725 and the physical
model 727 are used as inputs for the generation of a ML model 728.
For example, the statistical model 725 and the physical model 727
may be inputs for a ML model engine 729. The ML model engine 729
processes the physical model 727 and the statistical model 725 and
outputs the ML model 728. In some embodiments, the physical model
727 may be used to derive some physical measurements that cannot be
measured, and the physical model 727 outputs may be considered as
additional inputs to the statistical model. In such situations, the
ML model engine 729 adds the information from the physical model
727 to the statistical model 725 to provide the ML model 728. The
ML model 728, therefore, allows for the two models 725 and 727 to
be used for validation of individual points in the process space,
and provides a more complete process space that can be individually
tailored to a given processing tool. However, in some embodiments,
the physical model 727 and the statistical model 725 may be
standalone models, depending on the outputs. That is, in some
embodiments, the statistical model 725 and the physical model 727
may not be merged into a ML model.
[0103] In an embodiment, the ML model may also be considered as
another instance of a statistical model 725. For example, in FIG.
7B, the physical model 727 output by the physical model engine 726
may be used as an input for the statistical model engine 724. The
statistical model engine 724 therefore has additional inputs in
order to generate a statistical model 725 that includes information
from the physical model 727. Particularly, the statistical model
engine 724 may already include the data from the physical model
727, and the use of a ML model engine to produce a ML model may not
be necessary in all embodiments.
[0104] Referring now to FIG. 8 a flow diagram illustrating a
process 870 for developing a process recipe using a ML model is
shown, in accordance with an embodiment. The targeted process
recipe is a process recipe having a set of process parameters that
will result in desired device outcomes on the wafer. In an
embodiment, the process 870 may begin with operation 871, which
includes determining desired device outcomes. In an embodiment, the
device outcomes may be on wafer device dimensions, material
compositions, or the like. For example, the device outcomes may
include a layer thickness, a thickness uniformity across the wafer,
a material composition of a layer, or a material composition
uniformity for the stacked memory device shown in FIG. 5.
[0105] In an embodiment, process 870 may continue with operation
872, which comprises querying a ML model to select a set of
processing parameters. In an embodiment, the ML model may be a
model of a process space generated from the combination of a
statistical model and a physical model. The statistical model may
be generated using a DoE of actual wafers as described above. The
physical model may be based on real world physics and chemical
equations. For example, the physical model may be generated from a
simulation of physical and chemical interactions within the
processing tool across a plurality of different processing
parameters. In an embodiment, the ML model may cover an entire
process space available to the processing tool.
[0106] The ML model allows for a stable process recipe to be
identified without relying solely on the experience and knowledge
of a process engineer. Instead, a baseline recipe that is expected
to produce device outcomes that closely match the targeted device
outcomes is able to be selected from the process space of the ML
model.
[0107] In an embodiment, process 870 may continue with operation
873, which comprises executing a small DoE to validate the model
recommendation. Due to the high precision of the ML model, a small
DoE (e.g., 20 or fewer wafers) may be all that is needed to
validate the model recommendation. In an embodiment, the DoE may be
designed by a process engineer. In another embodiment, the DoE may
be designed using the ML model.
[0108] In an embodiment, process 870 may continue with operation
874, which comprises measuring the DoE wafer results with one or
more metrology tools. The metrology data can be used to verify that
the targeted device outcomes have been achieved on the wafer.
[0109] In an embodiment, process 870 may continue with operation
875, which comprises determining if the desired device outcomes
have been achieved. If the desired device outcomes have been
achieved, then the process proceeds along to operation 876 and the
process is completed. If the desired device outcomes have not been
achieved, then the process may cycle or feedback to operation 872.
In an embodiment, the data from the small DoE may be fed back into
the ML model in order to update the ML model. For example, if the
process iteratively cycles back to operation 872, then DoEs
executed at operation 873 may be designed based on knowledge of
where the ML model is lacking (e.g., for a particular a process or
plasma chamber) based on additional knowledge learned from the DoEs
executed in the prior cycles. The updated ML model may then be
queried to provide a second baseline recipe. In this manner, even
when the first iteration is not successful, the process may still
converge to the proper recipe quickly, without the need for
extensive DoE and wasted resources.
[0110] Referring now to FIG. 9, a flow diagram illustrating a
process 980 for baselining a processing tool is shown, in
accordance with an embodiment. In an embodiment, the baselining
process may be beneficial to account for chamber drift during the
processing of wafers in the processing tool. In an embodiment, the
baselining process may be implemented at any desired frequency. For
example, the process 980 may be implemented per lot, per planned
maintenance (PM) event, or when the processed wafers have device
outcomes that are outside of specified ranges.
[0111] In an embodiment, the process 980 may begin with operation
981, which comprises running a limited DoE of wafers with external
metrology to baseline chamber performance. In an embodiment, the
limited DoE may include twenty wafers or fewer. The limited DoE may
utilize the process recipe of record as a baseline. The external
metrology may include any metrology suitable to determine device
outcomes for the processed wafers. For example, in the case of an
oxidation process, ellipsometry may be used to investigate film
thickness and thickness uniformity across a wafer.
[0112] In an embodiment, the process 980 may continue with
operation 982, which comprises adding the device outcomes and other
metrology data to the ML model. The additional data added to the ML
model may be referred to as a calibration data set. The calibration
data set is used to update the ML model so that the ML model more
accurately reflects the current condition of the processing tool.
For example, the process 580 may include operation 583, which
comprises adjusting a model prediction to account for specific
chamber conditions. That is, the process space of the ML model is
updated to more closely match the conditions of the processing tool
being investigated.
[0113] In an embodiment, the ML model may be a model of a process
space generated from the combination of a statistical model and a
physical model. The statistical model may be generated using a DoE
of actual wafers as described above. The physical model may be
based on real world physics and chemical equations. For example,
the physical model may be generated from a simulation of physical
and chemical interactions within a processing tool such as the
plasma treatment chamber with rotating crossflows across a
plurality of different processing parameters. In an embodiment, the
ML model may cover an entire process space available to the
processing tool.
[0114] In an embodiment, process 980 may continue with operation
984, which comprises predicting optimized process parameters to
achieve a desired wafer outcome of wafers subsequently processed in
the chamber. The optimized process parameters may be selected after
the ML model has been updated to include the calibration data set.
Accordingly, the new process recipe provides wafer parameters that
result in wafer outcomes that are more closely matched to the
targeted values, despite changes to the chamber condition. As such,
chamber drift may be monitored and accounted for in order to
maintain a tight process window and increase uniformity,
repeatability, and yield. Additionally, unscheduled downtime of the
tool is reduced since the processing recipe can be accurately
adjusted to account for chamber drift. Furthermore, when PM does
occur, process 980 may be implemented to provide a shorter recovery
time, which improves tool utilization.
[0115] In an embodiment, a ML model may further be used to provide
continuous (or near continuous) revision of a processing recipe to
account for chamber drift. For example, wafer and process data
obtained during the processing of device wafers may be obtained and
used to update the ML model. That is, a dedicated DoE may not be
necessary to provide a calibration data set. Wafer data from device
wafers may be obtained for every wafer or for a subset of the
wafers being processed.
[0116] Such an embodiment, may include a providing a ML model of a
processing tool. The ML model may include a statistical model and a
physical model that is similar to the ML models described above. In
an embodiment, the process may begin with a recipe being executed
in the processing tool to process a first wafer. After processing
the first wafer, wafer data from the first wafer and process data
from the processing tool relating to the execution of the recipe
may be obtained. In an embodiment, the wafer data may comprise
metrology data, such as, but not limited to, a thickness, a
thickness uniformity, and a profile. In an embodiment, process data
may include data obtained from sensors within processing tool
and/or tool configuration information. In an embodiment, the wafer
data and the process data is provided to the ML model to generate
an updated ML model. In an embodiment, the updated ML model is used
to generate a modified recipe to account for chamber drift in the
processing tool. Embodiments may then include executing the
modified recipe in the processing tool to process a second wafer.
While processing of a single first wafer is described above, it is
to be appreciated that a plurality of first wafers may be processed
before the updated ML model is generated. In such an embodiment,
multiple sets of wafer data and process data may be used to
generate the updated ML model.
[0117] FIG. 10 illustrates a diagrammatic representation of a
machine in the exemplary form of a computer system 1000 within
which a set of instructions, for causing the machine to perform any
one or more of the methodologies described herein, may be executed.
In alternative embodiments, the machine may be connected (e.g.,
networked) to other machines in a Local Area Network (LAN), an
intranet, an extranet, or the Internet. The machine may operate in
the capacity of a server or a client machine in a client-server
network environment, or as a peer machine in a peer-to-peer (or
distributed) network environment. The machine may be a personal
computer (PC), a tablet PC, a set-top box (STB), a web appliance, a
server, a network router, switch or bridge, or any machine capable
of executing a set of instructions (sequential or otherwise) that
specify actions to be taken by that machine. Further, while only a
single machine is illustrated, the term "machine" shall also be
taken to include any collection of machines (e.g., computers) that
individually or jointly execute a set (or multiple sets) of
instructions to perform any one or more of the methodologies
described herein.
[0118] The exemplary computer system 1000 includes a processor
1002, a main memory 1004 (e.g., read-only memory (ROM), flash
memory, dynamic random access memory (DRAM) such as synchronous
DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 1006
(e.g., flash memory, static random access memory (SRAM), MRAM,
etc.), and a secondary memory 1018 (e.g., a data storage device),
which communicate with each other via a bus 1030.
[0119] Processor 1002 represents one or more general-purpose
processing devices such as a microprocessor, central processing
unit, or the like. More particularly, the processor 1002 may be a
complex instruction set computing (CISC) microprocessor, reduced
instruction set computing (RISC) microprocessor, very long
instruction word (VLIW) microprocessor, processor implementing
other instruction sets, or processors implementing a combination of
instruction sets. Processor 1002 may also be one or more
special-purpose processing devices such as an application specific
integrated circuit (ASIC), a field programmable gate array (FPGA),
a digital signal processor (DSP), network processor, or the like.
Processor 1002 is configured to execute the processing logic 1026
for performing the operations described herein.
[0120] The computer system 1000 may further include a network
interface device 1008. The computer system 1000 also may include a
video display unit 1010 (e.g., a liquid crystal display (LCD), a
light emitting diode display (LED), or a cathode ray tube (CRT)),
an alphanumeric input device 1012 (e.g., a keyboard), a cursor
control device 1014 (e.g., a mouse), and a signal generation device
1016 (e.g., a speaker).
[0121] The secondary memory 1018 may include a machine-accessible
storage medium (or more specifically a computer-readable storage
medium) 1032 on which is stored one or more sets of instructions
(e.g., software 1022) embodying any one or more of the
methodologies or functions described herein. The software 1022 may
also reside, completely or at least partially, within the main
memory 1004 and/or within the processor 1002 during execution
thereof by the computer system 1000, the main memory 1004 and the
processor 1002 also constituting machine-readable storage media.
The software 1022 may further be transmitted or received over a
network 1020 via the network interface device 1008.
[0122] While the machine-accessible storage medium 1032 is shown in
an exemplary embodiment to be a single medium, the term
"machine-readable storage medium" should be taken to include a
single medium or multiple media (e.g., a centralized or distributed
database, and/or associated caches and servers) that store the one
or more sets of instructions. The term "machine-readable storage
medium" shall also be taken to include any medium that is capable
of storing or encoding a set of instructions for execution by the
machine and that cause the machine to perform any one or more of
the methodologies of the present disclosure. The term
"machine-readable storage medium" shall accordingly be taken to
include, but not be limited to, solid-state memories, and optical
and magnetic media.
[0123] In accordance with an embodiment of the present disclosure,
a machine-accessible storage medium has instructions stored thereon
which cause a data processing system to perform a method of
processing a wafer using insight from a ML model and/or a method of
updating or building a ML model.
[0124] Embodiments of a plasma chamber having a rotating modulated
cross-flow have been disclosed.
[0125] Example embodiment 1: A plasma treatment chamber, comprising
one or more sidewalls. A support surface within the one or more
sidewalls holds a workpiece. A first gas injector along the one or
more sidewalls injects a first gas flow in a first direction
generally parallel to and across a surface of the workpiece. A
first pump port along the one or more sidewalls generally opposite
of the first gas injector pumps out the first gas flow. A second
gas injector along the one or more sidewalls injects a second gas
flow in a second direction generally parallel to and across the
surface of the workpiece. A second pump port along the one or more
sidewalls generally opposite of the second gas injector pumps out
the second gas flow.
[0126] Example embodiment 2: The plasma treatment chamber of
embodiment 1, wherein the plasma treatment chamber is configured to
use the first and second gas injectors and the first and second
pump ports to rotate the first and second gas flows laterally
across the workpiece from the one or more sidewalls to provide a
multiphase rotating crossflow operation, the multiphase rotating
crossflow operation comprising at least a 2-phase cycle.
[0127] Example embodiment 3: The plasma treatment chamber of
embodiment 1, wherein the one or more sidewalls is cylindrical,
oval, square or rectangular in shape.
[0128] Example embodiment 4: The plasma treatment chamber of
embodiment 1, wherein the first gas injector and the second gas
injector are located in openings in the one or more sidewalls.
[0129] Example embodiment 5: The plasma treatment chamber of
embodiment 4, further comprising: a chamber lid over the one or
more sidewalls; a support pedestal that includes the support
surface, the support pedestal below the chamber lid and above a
chamber floor and surrounded by the one or more sidewalls; and
a processing region defined by an area between the chamber lid, the
support pedestal, and the one or more sidewalls.
[0130] Example embodiment 6: The plasma treatment chamber of
embodiment 5, wherein the first gas injector and the second gas
injector are located in the one or more sidewalls between the
chamber lid and the support pedestal.
[0131] Example embodiment 7: The plasma treatment chamber of
embodiment 5, wherein locations of the first pump port and the
second pump port are vertically offset from locations of the first
gas injector and the second gas injector by a distance
approximately equal to the distance between a bottom of the chamber
lid and the support pedestal.
[0132] Example embodiment 8: The plasma treatment chamber of
embodiment 5, wherein the first pump port and the second pump port
are in cavities between the one or more sidewalls and the support
pedestal, and above the chamber floor.
[0133] Example embodiment 9: The plasma treatment chamber of
embodiment 5, wherein the first pump port and the second pump port
are located in additional openings in the one or more sidewalls
between the chamber lid and the chamber floor.
[0134] Example embodiment 10: The plasma treatment chamber of
embodiment 1, wherein the first gas flow and the second gas flow
are switched on and off to control gas flow rotation.
[0135] Example embodiment 11: The plasma treatment chamber of
embodiment 1, further comprising a modulating function applied to a
flow rate of at least one of the first and second gas flows or
applied to an outlet conductance caused by at least one of the
first and second pump ports.
[0136] Example embodiment 12: The plasma treatment chamber of
embodiment 11, wherein the modulating function comprises one or
more gas inlet valves to modulate the flow rate of at least one of
the first and second gas flows.
[0137] Example embodiment 13: The plasma treatment chamber of
embodiment 12, wherein the one or more gas inlet valves are coupled
to one or more gas sources such that a single type of gas or a
mixture of different types of gases are injected into a processing
region during each rotation phase.
[0138] Example embodiment 14: The plasma treatment chamber of
embodiment 12, wherein the first and second gas injectors apply a
constant total gas flow to smoothly and sequentially inject gas
flows across different sides of the workpiece in a complete
cycle.
[0139] Example embodiment 15: The plasma treatment chamber of
embodiment 1, further comprising one or more throttle valves to
modulate pump port conductance or pressure of at least one of the
first and second pump ports.
[0140] Example embodiment 16: The plasma treatment chamber of
embodiment 15, wherein the one or more throttle valves operate
smoothly between two states of conductance or pressure, which are
cycled through in a like sequence as the first and second gas
injectors.
[0141] Example embodiment 17: The plasma treatment chamber of
embodiment 1, further comprising a top-down gas flow.
[0142] Example embodiment 18: The plasma treatment chamber of
embodiment 1, wherein the first gas injector and the first pump
port comprise a first injector-pump port pair, and the second gas
injector and the second pump port comprise a second gas inj
ector-pump port pair, wherein along a plane generally parallel to
an orientation of the workpiece, a location of the first
injector-pump port pair is offset by 180.degree. from a location
the second injector-pump port pair.
[0143] Example embodiment 19: The plasma treatment chamber of
embodiment 18, further comprising a top-down gas flow.
[0144] Example embodiment 20: The plasma treatment chamber of
embodiment 18, wherein the plasma treatment chamber further
comprises a third gas injector and an opposing third pump port to
provide a third injector-pump port pair and a 3-phase rotating
crossflow operation.
[0145] Example embodiment 21: The plasma treatment chamber of
embodiment 20, wherein the first injector-pump port pair, the
second injector-pump port pair and the third injector-pump port
pair are offset from one another by 120.degree..
[0146] Example embodiment 22: The plasma treatment chamber of
embodiment 20, wherein the first gas injector, the second gas
injector, and the third gas injector are located approximately
120.degree. from one another, and the first pump port, the second
pump port, and the third pump port are located 120.degree. from one
another, wherein the first pump port, the second pump port, and the
third pump port are dispersed laterally between the first gas
injector, the second gas injector, and the third gas injector.
[0147] Example embodiment 23: The plasma treatment chamber of
embodiment 20, further comprising a fourth gas injector and an
opposing fourth pump port to provide four injector-pump port pairs
and a 4-phase rotating crossflow operation.
[0148] Example embodiment 24: The plasma treatment chamber of
embodiment 23, wherein locations of each gas injector-pump port
pair along a circular sidewall is offset from adjacent
injector-pump port pair locations by an angle equal to 360 total
degrees divided by a number of injector-pump port pairs.
[0149] Example embodiment 25: The plasma treatment chamber of
embodiment 1, wherein at least one of the first gas injector and
the second gas injector comprises a single vent in the one or more
sidewalls.
[0150] Example embodiment 26: The plasma treatment chamber of
embodiment 1, wherein the first gas injector and the second gas
injector comprises a gas injector array of individual gas
injectors.
[0151] Example embodiment 27: The plasma treatment chamber of
embodiment 26, wherein the individual gas injectors are distributed
about a periphery of the one or more sidewalls, wherein sets of the
individual gas injectors are modulated by one or more gas inlet
valves to create gas flows in various directions across the
workpiece.
[0152] Example embodiment 28: The plasma treatment chamber of
embodiment 1, wherein at least one of the first gas injector and
the second gas injector comprises a gas injector array of
individual gas injectors.
[0153] Example embodiment 29: The plasma treatment chamber of
embodiment 28, further comprising a center-to-edge gas flow,
wherein at least the first gas flow or the second gas flow injected
from center ones of the individual gas injectors in the gas
injector array has a greater flow rate relative to edge ones in the
gas injector array.
[0154] Example embodiment 30: The plasma treatment chamber of
embodiment 28, further comprising an edge-to-center gas flow,
wherein at least the first gas flow or the second gas flow injected
from edge ones of the individual gas injectors in the gas injector
array has a greater flow rate relative to center ones in the gas
injector array.
[0155] Example embodiment 31: The plasma treatment chamber of
embodiment 28, further comprising at least four gas injector arrays
and opposing pump ports, wherein at least the first gas flow or the
second gas flow is directed to the sides of the workpiece rather
than across the workpiece by closing an opposing pump port and
opening side ones of the pump ports.
[0156] Example embodiment 32: The plasma treatment chamber of
embodiment 1, wherein the plasma treatment chamber is used to
perform reactive ion etching during semiconductor
manufacturing.
[0157] Example embodiment 33: A method of performing a rotating gas
cross-flow in a plasma treatment chamber. During a first phase the
steps include, injecting, by a first gas injector, a first gas flow
in a first direction generally parallel to and across a surface of
a device, and pumping out, by a first pump port, the first gas flow
from the plasma treatment chamber, wherein the first gas injector
is along one or more sidewalls of the plasma treatment chamber at a
first location, and the first pump port is along the one or more
sidewalls at a second location generally opposing the first gas
injector. During a second phase the steps include, injecting, by a
second gas injector, a second gas flow in a second direction
generally parallel to and across the surface of the device, and
pumping out, by a second pump port, the second gas flow from the
plasma treatment chamber, wherein the second gas injector is along
the one or more sidewalls at a third location, and the second pump
port is along the one or more sidewalls at a fourth location
generally opposing the second gas injector.
[0158] Example embodiment 34: The method of embodiment 33 further
comprising querying a machine learning (ML) model to control timing
of the first gas flow and the second gas flow.
[0159] Example embodiment 35: The method of embodiment 34 further
comprising developing a semiconductor manufacturing process recipe
for the device by: selecting one or more device outcomes; and
querying the ML model to obtain a process recipe recommendation
suitable for obtaining the device outcomes when processed by the
plasma treatment chamber with the rotating gas cross-flow.
[0160] Example embodiment 36: The method of embodiment 35 further
comprising executing a design of experiment (DoE) on a set of
wafers to validate the process recipe recommended by the ML
model.
[0161] Example embodiment 37: The method of embodiment 35 further
comprising receiving as the process recipe any combination of:
temperature, RF source power, bias power, gas pressure (mTorr), gas
flow ramp open times (msec), gas flow time (msec), gas flow ramp
closed and time (msec), gas flow fraction at various gas injectors,
gas composition at various injectors, gas flow fraction going to
various injectors, gas flow rotation frequency, gas flow
composition frequency, gas flow rate/velocity (pressure gradient),
gas flow direction, gas rotation phase, electron/plasma density,
plasma density gradient, electron temperature, ion current density,
plasma potential, sheath electric field potential, sheath electric
field tilt angle, sheath electric field z-component, mass fraction
atomic O, O flux, and Jion current density to workpiece.
[0162] Example embodiment 38: The method of embodiment 35 further
comprising selecting as the device outcomes any combination of: a
feature profile, a layer thickness, a thickness uniformity, a
material composition of a layer, a composition uniformity, a
porosity, a film stress, process uniformity across chambers in a
facility, wafer to wafer uniformity, and uniformity between
different wafer lots.
[0163] Example embodiment 39: The method of embodiment 38 further
comprising selecting as the device outcomes during an etch process
any combination of: etch rate, etch or uniformity center-to-edge,
etch rate uniformity azimuthal, etch feature uniformity, tilt,
bowel, and mask remaining.
[0164] Example embodiment 40: The method of embodiment 33 further
comprising baselining the plasma treatment chamber by running a
limited design of experiment (DoE) of wafers with external
metrology to baseline chamber performance. Wafer outcomes are and
metrology data from the limited DoE are added to a ML model as a
calibration data set, the ML model comprising a statistical model
and a physical model. A adjusting a model prediction adjusted to
account for specific chamber conditions and/or wafer conditions
identified by the limited DoE. Optimized process parameters are
predicted to achieve a desired wafer outcome for wafers processed
in the plasma treatment chamber.
[0165] Example embodiment 41: Embodiments disclosed herein include
a plasma treatment chamber, comprising one or more sidewalls. A
support within the one or more sidewalls to hold a workpiece. A
first gas injector is along the one or more sidewalls at a first
location, and a first pump port is along the one or more sidewalls
at a second location generally opposing the first gas injector. A
second gas is injector along the one or more sidewalls at a third
location, and second pump port is along the one or more sidewalls
at a fourth location generally opposing the second gas injector. A
multiphase rotating cross-flow operation comprises at least a first
phase and a second phase. The first phase comprises injecting, by
the first gas injector, a first gas flow in a first direction
generally parallel to and across a surface of the workpiece, and
pumping out, by the first pump port, the first gas flow. The second
phase comprises injecting, by the second gas injector, a second gas
flow in a second direction generally parallel to and across the
surface of the workpiece, and pumping out, by the second pump port,
the second gas flow.
[0166] Example embodiment 42: The plasma treatment chamber of
embodiment 41, further comprising a first gas valve coupled to the
first gas injector, a second gas valve coupled to the second gas
injector, a first pressure control valve coupled to the first pump
port, and a second pressure control valve coupled to the second
pump port.
[0167] Example embodiment 43: The plasma treatment chamber of
embodiment 42, further comprising a controller coupled to the
plasma treatment chamber, the controller configured to: during the
first phase, start the first gas flow by fully opening the first
gas valve and partially opening the second gas valve; and open the
first pressure control valve and close the second pressure control
valve.
[0168] Example embodiment 44: The plasma treatment chamber of
embodiment 43, wherein the controller is further configured to:
begin to close the first gas valve near a transition between the
first phase and the second phase, and rotate a direction of gas
flow by fully opening the second gas valve to begin the second
phase and partially opening the first gas valve; and open the
second pressure control valve and close the first pressure control
valve.
[0169] Example embodiment 44: A non-transitory computer readable
medium having stored thereon software instructions that, when
executed by a processor, cause the processor to rotate gas
cross-flow in a plasma treatment chamber, by executing the
following steps. During a first phase the steps include, injecting,
by a first gas injector, a first gas flow in a first direction
generally parallel to and across a surface of a device, and pumping
out, by a first pump port, the first gas flow from the plasma
treatment chamber, wherein the first gas injector is along one or
more sidewalls of the plasma treatment chamber at a first location,
and the first pump port is along the one or more sidewalls at a
second location generally opposing the first gas injector. During a
second phase the steps include, injecting, by a second gas
injector, a second gas flow in a second direction generally
parallel to and across the surface of the device, and pumping out,
by a second pump port, the second gas flow from the plasma
treatment chamber, wherein the second gas injector is along the one
or more sidewalls at a third location, and the second pump port is
along the one or more sidewalls at a fourth location generally
opposing the second gas injector.
[0170] Example embodiment 46. The non-transitory computer readable
medium of embodiment 45 further comprising querying a machine
learning (ML) models to control timing of the first gas flow and
the second gas flow.
[0171] Example embodiment 47: The non-transitory computer readable
medium of embodiment 46 further comprising developing a
semiconductor manufacturing process recipe for the device by:
selecting one or more device outcomes; and querying the ML model to
obtain a process recipe recommendation suitable for obtaining the
device outcomes when processed by the plasma treatment chamber with
a rotating gas cross-flow.
[0172] Example embodiment 48: The non-transitory computer readable
medium of embodiment 47 further comprising executing a design of
experiment (DoE) on a set of wafers to validate the process recipe
recommended by the ML model.
[0173] Example embodiment 49: The non-transitory computer readable
medium of embodiment 47 further comprising receiving as the process
recipe any combination of: temperature, RF source power, bias
power, gas pressure (mTorr), gas flow ramp open times (msec), gas
flow time (msec), gas flow ramp closed and time (msec), gas flow
fraction at various gas injectors, gas composition at various
injectors, gas flow fraction going to various injectors, gas flow
rotation frequency, gas flow composition frequency, gas flow
rate/velocity (pressure gradient), gas flow direction, gas rotation
phase, electron/plasma density, plasma density gradient, electron
temperature, ion current density, plasma potential, sheath electric
field potential, sheath electric field tilt angle, sheath electric
field z-component, mass fraction atomic O, O flux, and Jion current
density to workpiece.
[0174] Example embodiment 50: The non-transitory computer readable
medium of embodiment 47 further comprising selecting as the device
outcomes any combination of: a feature profile, a layer thickness,
a thickness uniformity, a material composition of a layer, a
composition uniformity, a porosity, a film stress, process
uniformity across chambers in a facility, wafer to wafer
uniformity, and uniformity between different wafer lots.
[0175] Example embodiment 51: The non-transitory computer readable
medium of embodiment 50 further comprising selecting as the device
outcomes during an etch process any combination of: etch rate, etch
or uniformity center-to-edge, etch rate uniformity azimuthal, etch
feature uniformity, tilt, bowel, and mask remaining.
[0176] Example embodiment 52: The non-transitory computer readable
medium of embodiment 45 further comprising baselining the plasma
treatment chamber by running a limited design of experiment (DoE)
of wafers with external metrology to baseline chamber performance.
Wafer outcomes and metrology data from the limited DoE to a ML
model are added as a calibration data set, wherein the ML model
comprises a statistical model and a physical model. A model
prediction is adjusted to account for specific chamber conditions
and/or wafer conditions identified by the limited DoE. Optimized
process parameters are predicted to achieve a desired wafer outcome
for wafers processed in the plasma treatment chamber.
* * * * *