U.S. patent application number 14/716838 was filed with the patent office on 2016-11-24 for methods and systems for applying run-to-run control and virtual metrology to reduce equipment recovery time.
The applicant listed for this patent is Applied Materials, Inc.. Invention is credited to Parris C.M. Hawkins, Jimmy Iskandar, James Moyne, Jianping Zou.
Application Number | 20160342147 14/716838 |
Document ID | / |
Family ID | 57325401 |
Filed Date | 2016-11-24 |
United States Patent
Application |
20160342147 |
Kind Code |
A1 |
Iskandar; Jimmy ; et
al. |
November 24, 2016 |
METHODS AND SYSTEMS FOR APPLYING RUN-TO-RUN CONTROL AND VIRTUAL
METROLOGY TO REDUCE EQUIPMENT RECOVERY TIME
Abstract
Described herein are methods, apparatuses, and systems for
reducing equipment repair time. In one embodiment, a computer
implemented method includes collecting, with a system, data
including test substrate data or other metrology data and fault
detection data for maintenance recovery of at least one
manufacturing tool in a manufacturing facility and determining,
with the system, a relationship between tool parameter settings for
the at least one manufacturing tool and at least some collected
data including the test substrate data. The method further includes
utilizing zero or more virtual metrology predictive algorithms and
at least some collected data to obtain a metrology prediction and
applying multivariate run-to-run (R2R) control modeling to obtain a
state estimation including a current operating region of the at
least one manufacturing tool based on the test substrate data and
obtain at least one tool parameter adjustment for at least one
target parameter for the at least one manufacturing tool. Applying
multivariate run-to-run (R2R) control modeling to obtain tool
parameter adjustments for at least one manufacturing tool occurs
after maintenance to reduce maintenance recovery time and to reduce
requalification time.
Inventors: |
Iskandar; Jimmy; (Fremont,
CA) ; Zou; Jianping; (Austin, TX) ; Hawkins;
Parris C.M.; (Los Altos, CA) ; Moyne; James;
(Canton, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Applied Materials, Inc. |
Santa Clara |
CA |
US |
|
|
Family ID: |
57325401 |
Appl. No.: |
14/716838 |
Filed: |
May 19, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05B 23/024 20130101;
G05B 23/0294 20130101 |
International
Class: |
G05B 19/402 20060101
G05B019/402; G05B 19/404 20060101 G05B019/404 |
Claims
1. A computer implemented method comprising: collecting, with a
system, data including test substrate data or other metrology data
and fault detection data for maintenance recovery of at least one
manufacturing tool in a manufacturing facility; determining, with
the system, a relationship between tool parameter settings for the
at least one manufacturing tool and at least some collected data
including the test substrate data; utilizing zero or more virtual
metrology predictive algorithms and at least some collected data to
obtain a metrology prediction; and applying multivariate run-to-run
(R2R) control modeling to obtain a state estimation including a
current operating region of the at least one manufacturing tool
based on the test substrate data and obtain at least one tool
parameter adjustment for at least one target parameter for the at
least one manufacturing tool, wherein applying multivariate
run-to-run (R2R) control modeling to obtain tool parameter
adjustments for at least one manufacturing tool occurs after
maintenance to reduce maintenance recovery time and to reduce
requalification time.
2. The computer implemented method of claim 1, wherein the R2R
control modeling utilizes the following parameters: sensor data
obtained from a sensor of the at least manufacturing tool, state at
time k, state at time k+1, sensor noise, metrology measurement
noise, metrology measurement at time k, a state transition matrix,
a process sensitivity matrix, and an observation model matrix.
3. The computer implemented in method of claim 1, wherein the
virtual metrology predictive algorithm is tuned prior to or during
its use in a tool parameter adjustment event of the at least one
tool parameter adjustment.
4. The computer implemented method of claim 1, wherein the
collected data includes a thickness profile and a dopant
concentration for maintenance recovery of a deposition tool.
5. The computer implemented method of claim 4, wherein the tool
parameter adjustments includes adjusting a temperature parameter,
lamp power ratios, and gas flow parameters for the deposition
tool.
6. The computer implemented method of claim 1, further comprising:
determining whether the test substrate data satisfies the at least
one target parameter.
7. A computer-readable storage medium comprising executable
instructions to cause a processor to perform operations, the
instructions comprising: collecting, with a system, data including
test substrate data or metrology data and fault detection data for
maintenance recovery of at least one manufacturing tool in a
manufacturing facility; determining, with the system, a
relationship between tool parameter settings for the at least one
manufacturing tool and at least some collected data including the
test substrate data; utilizing zero or more virtual metrology
predictive algorithms and at least some collected data to obtain a
metrology prediction; and applying multivariate run-to-run (R2R)
control modeling to obtain a state estimation including a current
operating region of the at least one manufacturing tool based on
the test substrate data and obtain at least one tool parameter
adjustment for at least one target parameter for the at least one
manufacturing tool, wherein applying multivariate run-to-run (R2R)
control modeling to obtain tool parameter adjustments for at least
one manufacturing tool occurs after maintenance to reduce
maintenance recovery time and to reduce requalification time
8. The computer-readable storage medium of claim 7, wherein the R2R
control modeling utilizes the following parameters: sensor data
obtained from a sensor of the at least manufacturing tool, state at
time k, state at time k+1, sensor noise, metrology measurement
noise, metrology measurement at time k, a state transition matrix,
a process sensitivity matrix, and an observation model matrix.
9. The computer-readable storage medium of claim 8, wherein the
virtual metrology predictive algorithm is tuned prior to or during
its use in a tool parameter adjustment event of the at least one
tool parameter adjustment.
10. The computer-readable storage medium of claim 7, wherein the
collected data includes a thickness profile and a dopant
concentration for maintenance recovery of a deposition tool.
11. The computer-readable storage medium of claim 10, wherein the
tool parameter adjustments includes adjusting a temperature
parameter, lamp power ratios, and gas flow parameters for the
deposition tool.
12. The computer implemented method of claim 7, further comprising:
determining whether the test substrate data satisfies the at least
one target parameter.
13. A computer system comprising: a memory to store one or more
sets of instructions; and a processor, coupled to the memory, is
configured to execute instructions to: determining, with the
system, a relationship between tool parameter settings for the at
least one manufacturing tool and at least some collected data
including the test substrate data; utilizing zero or more virtual
metrology predictive algorithms and at least some collected data to
obtain a metrology prediction; applying multivariate run-to-run
(R2R) control modeling to obtain a state estimation including a
current operating region of the at least one manufacturing tool
based on the test substrate data and obtain at least one tool
parameter adjustment for at least one target parameter for the at
least one manufacturing tool, wherein applying multivariate
run-to-run (R2R) control modeling to obtain tool parameter
adjustments for at least one manufacturing tool occurs after
maintenance to reduce maintenance recovery time and to reduce
requalification time.
14. The computer system of claim 13, wherein the R2R control
modeling utilizes the following parameters: sensor data obtained
from a sensor of the at least manufacturing tool, state at time k,
state at time k+1, sensor noise, metrology measurement noise,
metrology measurement at time k, a state transition matrix, a
process sensitivity matrix, and an observation model matrix.
15. The computer system of claim 14, wherein the virtual metrology
predictive algorithm is tuned prior to or during its use in a tool
parameter adjustment event of the at least one tool parameter
adjustment.
16. The computer system of claim 13, wherein the collected data
includes a thickness profile and a dopant concentration for
maintenance recovery of a deposition tool.
17. The computer system of claim 16, wherein the tool parameter
adjustments includes adjusting a temperature parameter, lamp power
ratios, and gas flow parameters for the deposition tool.
18. The computer system of claim 13, further comprising:
determining whether the test substrate data satisfies the at least
one target parameter.
Description
TECHNICAL FIELD
[0001] Embodiments of the present invention relate to methods and
systems for applying run-to-run control and virtual metrology to
reduce equipment recovery time including mean-time-to-repair (MTTR)
for equipment and components.
BACKGROUND
[0002] In manufacturing there are a number of processes where
maintenance is a requirement either at specific intervals or in
response to an event such as a broken component or low quality
production. Following the maintenance there is often a process that
is executed whereby the equipment is "requalified" to a certain
state such as "ready to return to production". This requalification
can be a long and iterative process whereby process and equipment
parameters are adjusted or "tuned". After a tuning iteration the
equipment is evaluated, e.g., by producing a test product and then
measuring the quality of the test product. If the evaluation
indicates that the equipment or process has not met certain
criteria another tuning iteration is conducted. This iterative
process is often manual, and even if partially automated, is often
addressed in a univariate adhoc fashion where a few of the total
set of parameters are tuned at each iteration. The time taken for
these tuning iterations is considered to be part of the
mean-time-to-repair (MTTR) for the equipment.
SUMMARY
[0003] Described herein are methods, apparatuses, and systems for
reducing equipment repair time. In one embodiment, a computer
implemented method includes collecting, with a system, data
including test substrate data or other metrology data and fault
detection data for maintenance recovery of at least one
manufacturing tool in a manufacturing facility and determining,
with the system, a relationship between tool parameter settings for
the at least one manufacturing tool and at least some collected
data including the test substrate data. The method further includes
utilizing zero or more virtual metrology predictive algorithms and
at least some collected data to obtain a metrology prediction and
applying multivariate run-to-run (R2R) control modeling to obtain a
state estimation including a current operating region of the at
least one manufacturing tool based on the test substrate data and
obtain at least one tool parameter adjustment for at least one
target parameter for the at least one manufacturing tool. Applying
multivariate run-to-run (R2R) control modeling to obtain tool
parameter adjustments for at least one manufacturing tool occurs
after maintenance to reduce maintenance recovery time and to reduce
requalification time.
[0004] In another embodiment, a computer system includes a memory
to store one or more sets of instructions and a processor that is
coupled to the memory. The processor is configured to execute
instructions to collect data including test substrate data or
metrology data and fault detection data for maintenance recovery of
at least one manufacturing tool in a manufacturing facility,
determine a relationship between tool parameter settings for the at
least one manufacturing tool and at least some collected data
including the test substrate data. The method further includes
utilizing zero or more virtual metrology predictive algorithms and
at least some collected data to obtain a metrology prediction and
applying multivariate run-to-run (R2R) control modeling to obtain a
state estimation including a current operating region of the at
least one manufacturing tool based on the test substrate data and
obtain at least one tool parameter adjustment for at least one
target parameter for the at least one manufacturing tool.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] The present invention is illustrated by way of example, and
not by way of limitation, in the figures of the accompanying
drawings and in which:
[0006] FIG. 1 is a time line of a maintenance recovery process in
accordance with one embodiment;
[0007] FIG. 2 illustrates an exemplary architecture of a
manufacturing environment 100 for reducing maintenance time (e.g.,
MTTR) in accordance with one embodiment;
[0008] FIG. 3 illustrates a flow diagram of one embodiment for a
computer implemented method of multivariate analysis utilizing
run-to-run control and virtual metrology to reduce MTTR and improve
G2G time during post preventative maintenance (PM) recovery;
[0009] FIG. 4A illustrates a plot 400 of thickness versus gas flow
for different temperatures of a deposition tool in accordance with
one embodiment;
[0010] FIG. 4B illustrates a plot 420 of thickness versus gas flow
for a specific temperature T* of a deposition tool at post PM in
accordance with one embodiment;
[0011] FIG. 4C illustrates a plot 440 of thickness versus gas flow
for a temperature T* of a deposition tool in accordance with one
embodiment, and finding the recommended gas flow for a desired
thickness target;
[0012] FIG. 5A illustrates lamp failure modes in accordance with
one embodiment;
[0013] FIG. 5B illustrates a diagram in which multivariate R2R
control with VM is applied in accordance with one embodiment;
[0014] FIG. 6 illustrates a diagram in which multivariate R2R and
VM models are applied during maintenance recovery in accordance
with one embodiment;
[0015] FIG. 7 illustrates a diagram in which multivariate R2R
control with VM is applied in accordance with one embodiment;
[0016] FIG. 8 illustrates a diagram in which multivariate R2R
control with VM is applied in accordance with one embodiment;
[0017] FIG. 9 illustrates an exemplary architecture of a system
(e.g., an equipment engineering system (EES)), in accordance with
one embodiment; and
[0018] FIG. 10 illustrates a block diagram of an exemplary computer
system, in accordance with one embodiment of the present
invention.
DETAILED DESCRIPTION
[0019] Described herein are methods, apparatuses, and systems for
multivariate analysis utilizing run-to-run control and virtual
metrology to reduce MTTR during post preventative maintenance (PM)
recovery. In some embodiments, systems and methods for reducing the
time for tuning iterations (e.g., by reducing the needed number of
iterations) results in reduced MTTR and reduced green-to-green
(G2G) time (i.e., the time between production-worthy states).
Embodiments of this invention reduce the MTTR and G2G time by
reducing the number of tuning iterations required to bring an
equipment or manufacturing tool to a specified state after a
maintenance or other non-production event. The methods and systems
of the present disclosure leverage capabilities that include
"run-to-run" (R2R) control and virtual metrology (VM).
[0020] Following maintenance there is often a process that is
executed in which the equipment is "requalified" to a certain state
such as a ready to return to production state. FIG. 1 is a time
line of a maintenance recovery process in accordance with one
embodiment. There are a number of techniques that can be used
during the production cycle (from producing wafers during
production state 2, through predicting and scheduling maintenance
state 4, seasoning state 6, requalification state 8 and returning
to a production state 9 after a maintenance event) that can improve
production capabilities. MTBI is defined as
mean-time-between-interrupts for a production state 2. Many of
these techniques are components or extensions of existing Advanced
Process Control (APC) systems capabilities and can therefore
leverage the data management environment provided by an existing
manufacturing system's APC infrastructure. The specific
capabilities of the solution, their definitions, and example of
their manner of utilization are described as follows.
[0021] Fault Detection (FD) is the technique of monitoring and
analyzing variations in tool and/or process data to detect
anomalies. Fault detection includes both univariate and
multivariate statistical analysis techniques. FD analysis is often
used to identify excursions. Also FD analysis output feed EHM, PdM
and VM solutions (see below).
[0022] Equipment Health Monitoring (EHM) is the technology of
monitoring tool parameters to assess the tool health as a function
of deviation from normal behavior. EHM is not necessarily
predictive in nature, but is often a component of predictive
systems. EHM can be used during production (e.g., t.sub.10) to
monitor tool health and during the maintenance recovery process to
assess "fingerprints" indicating successful maintenance procedures
(e.g., t.sub.40), ready to move to requalification (e.g., t.sub.50)
or during requalification (e.g., t.sub.60) to help determine if a
component is ready to return to a production state (e.g.,
maintenance success verification).
[0023] Predictive Maintenance (PdM) is the technology of utilizing
process and equipment state information to predict when a tool or a
particular component in a tool might need maintenance, and then
utilizing this prediction as information to improve maintenance
procedures. This could mean predicting and avoiding unplanned
downtimes and/or relaxing un-planned downtime schedules by
replacing schedules with predictions. PdM solutions (e.g., PdM at
t.sub.22) have been illustrated to provide a number of benefits
including reduction of unscheduled downtime.
[0024] Run-to-Run (R2R) control is the technique of modifying
recipe or other equipment parameters, or the selection of control
parameters between runs to improve processing performance. A "run"
can be a batch, lot, or an individual substrate, wafer, or other
product. R2R control (e.g., t.sub.20) is typically used during
production to improve processes through improved closeness to
quality targets and reduce variability of quality parameters. R2R
control (e.g., t.sub.30) can also be used during a maintenance
state to determine maintenance settings or process adjustments.
[0025] Virtual Metrology (VM) is the technology of prediction of
post process metrology variables (e.g., either measurable or
nonmeasurable) using process and wafer state information that could
include upstream metrology and/or sensor data. Typical uses of VM
are to enhance the R2R control capabilities (e.g., t.sub.10,
t.sub.30) and reduce average production cycle time by reducing the
need for metrology. Best practices and domain knowledge are
procedures that leverage understanding of or experience with the
equipment and process and related components to improve
capabilities throughout the production cycle.
[0026] In the following description, numerous details are set
forth. It will be apparent, however, to one skilled in the art,
that the present invention may be practiced without these specific
details. In some instances, well-known structures and devices are
shown in block diagram form, rather than in detail, in order to
avoid obscuring the present invention.
[0027] Some portions of the detailed descriptions which follow are
presented in terms of algorithms and symbolic representations of
operations on data bits within a computer memory. Unless
specifically stated otherwise, as apparent from the following
discussion, it is appreciated that throughout the description,
discussions utilizing terms such as "collecting", "predicting",
"performing", "adjusting", "comparing", or the like, refer to the
action and processes of a computer system, or similar electronic
computing device, that manipulates and transforms data represented
as physical (electronic) quantities within the computer system's
registers and memories into other data similarly represented as
physical quantities within the computer system memories or
registers or other such information storage, transmission or
display devices.
[0028] Embodiments of the present invention also relates to an
apparatus for performing the operations herein. This apparatus may
be specially constructed for the required purposes, or it may
comprise a general purpose computer selectively activated or
reconfigured by a computer program stored in the computer. Such a
computer program may be stored in a computer readable storage
medium, such as, but not limited to, any type of disk including
floppy disks, optical disks, CD-ROMs, and magnetic-optical disks,
read-only memories (ROMs), random access memories (RAMs), EPROMs,
EEPROMs, magnetic or optical cards, or any type of media suitable
for storing electronic instructions, each coupled to a computer
system bus.
[0029] The algorithms and displays presented herein are not
inherently related to any particular computer or other apparatus.
Various general purpose systems may be used with programs in
accordance with the teachings herein, or it may prove convenient to
construct more specialized apparatus to perform the required method
steps. The required structure for a variety of these systems will
appear as set forth in the description below. In addition, the
present invention is not described with reference to any particular
programming language. It will be appreciated that a variety of
programming languages may be used to implement the teachings of the
invention as described herein.
[0030] The present invention may be provided as a computer program
product, or software, that may include a machine-readable medium
having stored thereon instructions, which may be used to program a
computer system (or other electronic devices) to perform a process
according to the present invention. A machine-readable medium
includes any mechanism for storing information in a form readable
by a machine (e.g., a computer). For example, a machine (e.g., a
computer) readable storage medium includes read only memory
("ROM"), random access memory ("RAM"), magnetic disk storage media,
optical storage media, flash memory devices, etc.
[0031] FIG. 2 illustrates an exemplary architecture of a
manufacturing environment 100 for reducing maintenance time (e.g.,
MTTR), in accordance with one embodiment. The manufacturing
environment 100 may be a semiconductor manufacturing environment,
an automotive manufacturing environment, aerospace equipment
manufacturing environment, medical equipment manufacturing
environment, display and solar manufacturing environment, etc. In
one embodiment, the manufacturing environment 100 includes an
equipment engineering system (EES) 105, a VM multi-algorithm
predictive subsystem 107 within the EES system 105 or some other
system coupled to the EES via a network, a manufacturing execution
system (MES) 110, a yield management system (YMS) 120 and a
consolidated data store 115. The EES 105, MES 110, YMS 120 and
consolidated data store 115 may be connected via a network (not
shown), such as a public network (e.g., Internet), a private
network (e.g., Ethernet or a local area Network (LAN)), or a
combination thereof.
[0032] The manufacturing execution system (MES) 110 is a system
that can be used to measure and control production activities in a
manufacturing environment. The MES 110 may control some production
activities (e.g., critical production activities) or all production
activities of a set of manufacturing equipment (e.g., all
photolithography equipment in a semiconductor fabrication
facility), of a manufacturing facility (e.g., an automobile
production plant), of an entire company, etc. The MES 110 may
include manual and computerized off-line and/or on-line transaction
processing systems. Such systems may include manufacturing
machines, metrology devices, client computing devices, server
computing devices, databases, etc. that may perform functions
related to processing.
[0033] In one embodiment, the MES 110 is connected with a
consolidated data store 115. The consolidated data store 115 may
include databases, file systems, or other arrangements of data on
nonvolatile memory (e.g., hard disk drives, tape drives, optical
drives, etc.), volatile memory (e.g., random access memory (RAM)),
or combination thereof. In one embodiment, the consolidated data
store 115 includes data from multiple data stores (e.g., a YMS data
store, a maintenance data store, a metrology data store, process
data stores, etc.) that are interconnected. The consolidated data
store 115 may store, for example, historical process information of
manufacturing recipes (e.g., temperatures, pressures, chemicals
used, process times, etc.), equipment maintenance histories,
inventories, etc. The consolidated data store 115 may also store
data generated by the MES 110, YMS 120 and/or EES 105. For example,
the EES 105 may store fault detection and characterization data in
the consolidated data store 115, the YMS 120 may store yield
analysis data in the consolidated data store 115, and the MES 110
may store historical process information in the consolidated data
store 115. This permits each of the YMS 120, EES 105 and MES 110 to
leverage data generated by the other systems. The consolidated data
store 115 may reside on one or more computing devices hosting any
of the MES 110, the YMS 120 and EES 105, or on one or more
different computing devices.
[0034] The EES 105 is a system that manages some or all operations
of a manufacturing environment (e.g., factory). The EES 105 may
include manual and computerized off-line and/or on-line transaction
processing systems that may include client computing devices,
server computing devices, databases, etc. that may perform the
functions of equipment tracking, dispatching (e.g., determining
what material goes to what processes), product genealogy, labor
tracking (e.g., personnel scheduling), inventory management,
costing, electronic signature capture, defect and resolution
monitoring, key performance indicator monitoring and alarming,
maintenance scheduling, and so on.
[0035] The EES 105 draws inferences from, reports out, and/or acts
upon the combined information that is collected and stored in the
consolidated data store 115 and/or the metrology data and process
data that is reported by the MES 110. For example, EES 105 can act
as an early warning system (e.g., predict scrap, initiate product
rework, etc.), provide bottleneck analysis, provide asset
management (e.g., reduce unscheduled equipment downtime, reduce
scheduled equipment downtime, reduce MTTR), improve lean practices,
etc. The EES 105 can be used to gain an understanding of the
manufacturing environment 100, and can enable a user to determine
an efficiency of the manufacturing environment 100 and/or how to
improve all or components of the manufacturing environment 100. In
one embodiment, the EES 105 includes components (e.g., VM
multi-algorithm predictive subsystem 107 having VM module with
prediction algorithm switching module, multivariate R2R controller
112, etc.) that enable the EES 105 to utilize and determine
predictive algorithms for adaptive virtual metrology, perform R2R
control, reduce MTTR, and reduce green-to-green (G2G) time, which
is a time period between production production-worthy states.
[0036] The yield management system (YMS) 120 analyzes end-of-line
data such as electronic test (e-test) data to determine product
yield. The end-of-line data may include wafer acceptance testing
(WAT), wafer sort results and/or final test operations. The yield
manager 120 can provide product yield trends, lot level analysis of
product yield, yield correlation to manufacturing processes,
statistical analysis of yield, etc. In one embodiment, the YMS 120
uses integrated circuit design, visible defect, parametric and
e-test data to identify causes of low yield.
[0037] In one example, with many maintenance events in
semiconductor manufacturing, process "tuning" is required as part
of the maintenance recovery process, where test wafers are
processed and measured and the process is adjusted based on the
results. The process is determined ready-for-production when the
test wafer measurements meet specified quality criteria. This
tuning can oftentimes be costly both in terms of wafers and lost
production time. The tuning process itself can often be inexact
with adjustments determined manually and often in a univariate
(one-by-one or a-few-by-one) fashion.
[0038] In one embodiment, the tuning process can be improved by
utilizing multivariate R2R control along with VM (as necessary) to
more precisely determine tuning recommendations and reduce tuning
iteration steps. A multivariate analysis is based on a statistical
principle of multivariate statistics in which observation and
analysis of more than one statistical outcome variable occurs at a
time.
[0039] FIG. 3 illustrates a flow diagram of one embodiment for a
computer implemented method of multivariate analysis utilizing
run-to-run control and virtual metrology to reduce MTTR and improve
G2G time during post preventative maintenance (PM) recovery. The
method may be performed by processing logic that may comprise
hardware (e.g., circuitry, dedicated logic, programmable logic,
microcode, etc.), software (such as instructions run on a
processing device), or a combination thereof. In one embodiment, a
computer implemented method 300 is performed by the equipment
engineering system 105 or some other system (e.g., a system hosting
a VM multi-algorithm prediction subsystem 107, R2R controller, and
coupled to the EES 105 via a network). The computer implemented
method 300 is designed to transition a manufacturing tool to an
ideal or nearly ideal operating region after PM (or any
maintenance) with some constraints. The ideal or nearly ideal
operating region is defined by key parameters (e.g., thickness
profile, electrical properties, etc.). Constraints may include
tuning parameters (e.g., gas flows, temperature) that have certain
boundaries and possible relationships with other tuning parameters
or variables in order for the tool to be qualified for a production
state. The tool is transitioned to the production state typically
using multiple iterations of test substrates and adjusting tuning
parameters.
[0040] Referring to FIG. 3, the computer implemented method 300
includes processing test substrates with nominal values of tuning
parameters (or values defined by an operator of a manufacturing
tool such as a process engineer or technician) of at least one
manufacturing tool at operation 301. The computer implemented
method 300 includes collecting data (e.g., test substrate data
obtained from measurements of the test substrates, other metrology
data, fault detection data, thickness statistical process control
(SPC) data, etc.) by a system (e.g., an equipment engineering
system) at operation 302. The collected data includes data
associated with a manufacturing process, the at least one
manufacturing tool and/or a manufactured product. Processing logic
of the system determines a relationship between tool parameter
settings (e.g., temperature, lamp power ratios, gas flows during
process recipes, chamber pressure, downforce for a chemical
mechanical planarization tool, etc.) for the at least one
manufacturing tool and the collected data (e.g., test substrate
data, other metrology data, fault detection data, thickness
statistical process control (SPC) data, etc.) at operation 304.
Processing logic then compares test substrate data to at least one
target parameter (e.g., a range of target values for each target
parameter) for the test substrates of the at least one
manufacturing tool at operation 305. If at least one target
parameter (e.g., electrical parameters) or metrology data is not
measured or available, then processing logic of the system utilizes
zero or more (or at least one) virtual metrology predictive
algorithms (e.g., Partial Least Squared (PLS), Support Vector
Regression (SVR), etc.) and at least some of the collected data to
obtain a metrology prediction for the at least one target parameter
or metrology data that is not measured or available at operation
306.
[0041] Processing logic of the system determines whether the test
substrate data satisfies the at least one target parameter (e.g.,
within a range of target values for each target parameter) at
operation 308. If so, then the processing logic completes the
requalification process at operation 320.
[0042] Otherwise, processing logic then applies R2R control
modeling (e.g., linear, nonlinear) to obtain a state estimation
including a current operating region and condition of the at least
one manufacturing tool based on the test substrate data (e.g.,
measurements obtained from the test substrates at operation 301)
and the corresponding tuning parameters applied during the
processing of the test substrates at operation 310. The processing
logic of the system provides recommended tool parameter adjustments
of a tool parameter adjustment event to move or transition the
current operating region of the at least manufacturing tool to an
ideal or nearly ideal operating region having ideal or nearly ideal
target parameters based on the R2R control modeling at operation
312. A virtual metrology predictive algorithm if virtual metrology
is necessary is tuned prior to or during its use in a tool
parameter adjustment event of the tool parameter adjustments The
method proceeds to operation 301 for a next iteration with the
recommended (or similar) tool parameter adjustments having at least
one different tuning parameter than the initial iteration at
operation 301. In this manner, the method 300 reduces a tool or
component downtime after PM or unplanned maintenance during
maintenance recovery and requalification which results in higher
product output. Thus, better utilization of manufacturing tools
increases profits for the manufacturing environment.
[0043] In another example, the method 300 does not include
utilizing one or more virtual metrology predictive algorithms to
obtain a metrology prediction for the at least one target parameter
that is not measured or available at operation 306. FIGS. 4A-4C
illustrate one implementation of the method 300 for a deposition
tool in accordance with one embodiment. FIG. 4A illustrates a plot
400 of thickness versus gas flow for different temperatures of a
deposition tool in accordance with one embodiment. The different
temperatures includes T1, T2, and T3. R2R control modeling uses
this plot 400 to determine a current operating region and condition
of the deposition tool based on the test substrate data (e.g.,
thickness measurements obtained from the test substrates, operation
301) illustrated in FIG. 4A and the corresponding tuning parameters
(e.g., temperature, gas flow) applied during the processing of the
test substrates. If test substrate data is not available, then VM
can be used for predicting metrology data. The R2R control modeling
models this plot 400 with the following equation:
[ y CentralThickness y Ge % ] = [ f 1 ( x GeH 4 , x DCS , x HCI , x
B 2 H 6 ) f 2 ( x GeH 4 , x DCS , x HCI , x B 2 H 6 ) ]
##EQU00001##
[0044] FIG. 4B illustrates a plot 420 of thickness versus gas flow
for a temperature T* of a deposition tool in accordance with one
embodiment. R2R control modeling obtains a state estimation
including a current operating region and condition of the
deposition tool as illustrated in plot 420. The R2R control model
estimate state for the deposition tool with the following
equation:
[ y CentralThickness y Ge % ] = [ f 1 * ( x GeH 4 , x DCS , x HCI ,
x B 2 H 6 ) f 2 ( x GeH 4 , x DCS , x HCI , x B 2 H 6 ) ]
##EQU00002##
[0045] FIG. 4C illustrates a plot 440 of thickness versus gas flow
for a temperature T* of a deposition tool for modeling predictive
control in accordance with one embodiment. This plot 440 includes a
target 450 having a target central thickness 452 and a target gas
flow 454 (e.g., target Ge dopant % for set of gas flows). R2R
control modeling provides recommended tool parameter adjustments to
move or transition the current operating region of the deposition
tool to an ideal or nearly ideal operating region having ideal or
nearly ideal target parameters based on the R2R control modeling.
The R2R control modeling models the ideal or nearly ideal operating
region for the deposition tool with the following equation:
[ x GeH 4 x DCS x HCI x B 2 H 6 ] = min X GeH 4 , X DCS , X HCI , X
B 2 H 6 { y CentralThickness Target - f 1 * ( x GeH 4 , x DCS , x
HCI , x B 2 H 6 ) y Ge % Target - f 2 * ( x GeH 4 , x DCS , x HCI ,
x B 2 H 6 ) } ##EQU00003##
[0046] For one example in semiconductor manufacturing, a R2R
control maintenance recovery approach can be applied to a thermal
process, where the lamp maintenance effort can be costly and time
consuming.
[0047] FIG. 5A illustrates lamp failure modes in accordance with
one embodiment. In a typical system, lamps can fail unexpectedly
causing unscheduled downtime and scrap. The lamp failure modes
include a filament 410 that is sagging below a center line, a short
circuit 412 between filament helix and support pillar, and a short
circuit 414 between turns. The maintenance recovery can be time
consuming as there are usually multiple post-maintenance (i.e.,
after lamp kit replacement) iterations of lamp parameter "tuning"
that include running a number of test wafers with specific
characterization recipes, analyzing metrology data, and making
hardware and software adjustments. This process continues until the
metrology data meets specified quality criteria. Four to ten
iterations of this type are not uncommon leading to MTTR on the
order of 2 days or more.
[0048] FIG. 5B illustrates a diagram in which multivariate R2R
control with VM is applied in accordance with one embodiment. PM
process metrology 502 is utilized along with VM models based on FD
output data 504 to determine a state of the system. PM tuning
models utilize this state information to determine tuning advices
in a multivariate fashion. The result is that fewer tuning
iterations are required to bring the chamber to a satisfactory
matched state for release back into production.
[0049] FIG. 6 illustrates a diagram in which multivariate R2R and
VM models are applied during maintenance recovery in accordance
with one embodiment. In one example, more than one tuning iteration
is usually required because the R2R control tuning model often has
to be re-centered with the first set of metrology results. This is
due to the variability in and length of time between PMs. Note also
that VM information used to enhance the determination of a system
state has been shown to provide an improved R2R control system
capability. However, depending on the maintenance event type and
tuning procedures, it may not always be necessary (i.e., PM process
metrology may be sufficient). The diagram 600 illustrates test
wafer quality (normalized) on a vertical axis versus tuning
iterations for chambers A and B on a horizontal axis. For chamber A
with no R2R control and VM, 5 iterations were need for maintenance
recovery. For chamber A with R2R control and VM, only 2 iterations
were need for maintenance recovery. Thus, the R2R control and VM
during the maintenance recovery reduces the MTTR by 3
iterations.
[0050] For chamber B with no R2R control and VM, 3 iterations were
need for maintenance recovery. For chamber B with R2R control and
VM, only 2 iterations were need for maintenance recovery. Thus, the
R2R control and VM during the maintenance recovery reduces the MTTR
by 1 iteration.
[0051] In one example of a thin film deposition PM (e.g., CVD PM,
epitaxial PM, etc.), a recovery period typically takes at least
five tuning iterations for testing processing recipes with test
substrates, performing metrology (e.g., measuring thickness
profiles, determining dopant concentrations, etc.) for multiple
recipes, and then making tuning adjustments for returning a film
deposition tool to a production state. This causes PM recovery time
period of greater than 3 days in which the deposition tool cannot
be used in the production state for producing product.
[0052] FIG. 7 illustrates a diagram in which multivariate R2R
control with VM is applied in accordance with one embodiment.
Process metrology data 702 (e.g., SPC data, test substrate data, FD
data, film thickness SPC data, etc.) and tool parameter and sensor
data for at least one manufacturing tool are received as inputs for
a multivariate prediction model 720 to determine state information
of at least one system, equipment, or manufacturing tool. Process
tuning models of the MVA prediction model utilize this state
information to determine tuning advices in a multivariate fashion.
The result is that fewer tuning iterations are required to bring
the system or tool to a satisfactory state for release back into a
production state.
[0053] In one example of MTTR modeling, a system collects
historical test substrate data (e.g., FD data, film thickness SPC
data, etc.). The system then determines a relationship between tool
parameters settings and SPC data that corresponds to the tool
parameter settings. Multivariate models are then utilized to
rapidly identify critical parameters of the manufacturing tool to
be adjusted or tuned. The models can identify values for
multivariate variables or parameters that are out of tool or
process specifications and then make appropriate corrections. In
this manner, a downtime (i.e., non-production state) of the
manufacturing tool is significantly reduced resulting in additional
product output. For example, a number of tuning iterations can be
reduced from at least 5 to 2 or 3 tuning iterations.
[0054] FIG. 8 illustrates a diagram in which multivariate R2R
control with VM is applied in accordance with one embodiment for
reducing maintenance recovery time. Process metrology data 802
(e.g., SPC data, test substrate data, FD data, film thickness SPC
data, etc.) and tool parameter and sensor data 804 for at least one
manufacturing tool are received as inputs for at least one of the
VM module 810 and the run-to-run control module 820. The VM module
810 utilizes at least one prediction algorithm for a virtual
metrology function which can be a linear or nonlinear function F
(u.sub.k) with u.sub.k being sensor data (e.g., temperature, lamp
power, lamp power ratios, gas flows for processing gases during a
processing recipe, etc.) for at least one manufacturing tool at
time k. The linear or nonlinear function F (u.sub.k) generates a
metrology prediction 812 based on at least the sensor data 804. The
metrology prediction 812 may also be based on metrology data 802.
The R2R controller 820 receives the metrology prediction 812 and
determines tool parameter adjustments 830 based on the metrology
prediction and R2R parameters including sensor data, a state
x.sub.k at time k (e.g., a state of the manufacturing tool at time
k such as), a state x.sub.k+1 at time k+1 (e.g., a state of the
manufacturing tool at time k+1), sensor noise w.sub.k, metrology
measurement noise v.sub.k, metrology measurement y.sub.k at time k,
a state transition function f(*), and observation function
g(*).
[0055] In one embodiment, the R2R controller 820 utilizes the
following equations for generating tool parameter adjustments
830:
x.sub.k+1=f(x.sub.k,u.sub.k,w.sub.k)
y.sub.k=g(x.sub.k)+v.sub.k
[0056] Improved knowledge of a state x.sub.k+1 at time k+1 (e.g., a
state of the manufacturing tool at time k+1) and identification of
critical parameters leads to a reduced number of tuning iterations
for tool parameters adjustments 830. The manufacturing tool can be
returned to a production state in a shorter time period with
reduced maintenance recovery and requalification.
[0057] In another embodiment, the R2R controller 820 does not need
the metrology prediction 812 for determining tool parameter
adjustments 830.
[0058] In this manner, the R2R controller 820 utilizes this state
information to determine tuning advices in a multivariate
fashion.
[0059] In one example, the VM module 810 and R2R controller 820 are
utilized to make adjustments to tool parameters based on a first
set of input parameters (e.g., 3-5 input parameters). After the
adjustments are made to tool parameters (e.g., process recipes)
then the VM module 810 and R2R controller 820 are again utilized to
make adjustments to tool parameters based on a different second set
of input parameters (e.g., 3-5 input parameters).
[0060] FIG. 9 illustrates an exemplary architecture of a system
(e.g., equipment engineering system (EES)), in accordance with one
embodiment. In one embodiment, the system 900 is implemented with
an Applied E3.TM. APC Infrastructure in which methods of the
present disclosure are integrated. The EES 900 leverages an E3
application adapter 610 that provides an interface to Web services.
Multivariate prediction module 920 can be integrated through Web
services. This multivariate prediction module 920 integration
approach enables rapid prototyping, customization, and technology
transfer. The multivariate prediction module 920 includes a
predictive VM module 922 that enables the EES 900 to utilize and
determine predictive algorithms for adaptive virtual metrology and
also R2R control module 924 for reducing tuning iterations for post
maintenance recovery.
[0061] The adapter 910 communicates with the strategy engine 930,
the client handler 940, the data service provider 950, and the log
server 960. The strategy engine 930 includes general blocks 931,
run to run blocks 932 (e.g., R2R controller, R2R module), FD blocks
933, EPT blocks 934, and custom blocks 935. The FD blocks 933
obtain FD data. The run to run blocks 932 include pre-configured or
adaptive R2R models for implemented operations of methods and
embodiments of the present disclosure. The EPT blocks 934 obtain
equipment performance tracking information. The data access layer
970 provides access to a database 980. This database 980 includes
process data, FDC/EPT/R2R data, control rules, and data collection
plans. The discovery manager 990 provides discovery features for
identifying capabilities integrated into the system. The strategy
engine is used to govern the interaction of blocks in terms of
"strategies" to achieve specific objectives in response to events
received.
[0062] For example, for substrate-to-substrate control (e.g.,
Wafer-to-Wafer (W2W) Control), a strategy housed by the strategy
engine 930 could be envisioned that captures FD outputs from a FD
implementation formulated with the FD blocks 933 and stored in the
database 980, sends this information to a formulation in the
multivariate prediction module 920 (integrated via the web-services
adaptor 910) for calculation of VM outputs (e.g., metrology
predictions), determination of tool parameter adjustments using R2R
924 (or alternatively R2R 932), and output this tool parameter
adjustments for reducing tuning iterations after PM and during
requalification state. Collected metrology data is used to update
VM models.
[0063] There are a number of extensions to prediction algorithms
that utilize feedback of actual output measurement data, such as
metrology or yield analysis, to continually improve or "tune" the
prediction models. As an example, NIPALS and EWMA (Exponentially
Weighted Moving Average) are two documented adaptive extensions to
the Project on Latent Structure prediction mechanisms. In one
embodiment of this invention, the VM algorithms are tuned as
necessary at the start of an MTTR event to account for changes in
process, equipment or other conditions between downtimes that
require an adjustment of the VM model. The type and level of
adjustment can be determined by techniques such as a VM switching
algorithm. These various extensions for handling the dynamics
perform differently depending on the prediction and adaptation
environment. Further many of the extensions also represent a
tradeoff between computational complexity or time, and
accuracy.
[0064] VM models include a predictive algorithm having a VM
prediction equation:
S=B*t+c
[0065] In some embodiments, S is a predicted output, B represents a
matrix, t is an input factor, and c is zero'th order term. S, B, t,
and c are components vectors or matrices. Given two prediction
adaptation algorithms EWMA and NIPALS, EWMA is fast and easy, but
can be inaccurate when the VM equation changes. The EWMA can
utilize zero'th order adaptation of the VM equation (e.g., updates
the "c" vector). NIPALS is complex, but more accurate. NIPALS
reformulates the VM equation (e.g., updates both "B" and "c"). The
VM multi-algorithm prediction subsystem 107 may compare predictions
of metrology data (Y') to actual metrology data (Y) on occasion
with this difference being E.
[0066] FIG. 10 illustrates a diagrammatic representation of a
machine in the exemplary form of a computer system 1000 in
accordance with one embodiment within which a set of instructions,
for causing the machine to perform any one or more of the
methodologies discussed 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 Personal Digital
Assistant (PDA), a cellular telephone, 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
discussed herein.
[0067] 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), etc.), and
a secondary memory 1018 (e.g., a data storage device), which
communicate with each other via a bus 1030.
[0068] 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 and steps discussed herein.
[0069] 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) 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).
[0070] The secondary memory 1018 may include a machine-readable
storage medium (or more specifically a computer-readable storage
medium) 1031 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 processing device 1002 during
execution thereof by the computer system 1000, the main memory 1004
and the processing device 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.
[0071] The machine-readable storage medium 1031 may also be used to
store one or more subsystems of a yield management system (YMS)
1020, an equipment engineering system (EES) 105 and/or a
manufacturing execution system (MES) 110 (as described with
reference to FIG. 1), and/or a software library containing methods
that call subsystems of a YMS, EES and/or MES. The machine-readable
storage medium 1031 may further be used to store one or more
additional components of a manufacturing information and control
system (MICS), such as a decision support logic component, a
real-time monitor, and/or an execution logic component. While the
machine-readable storage medium 1031 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 invention. 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.
In one embodiment, a computer system includes a memory to store one
or more sets of instructions and a processor that is coupled to the
memory. The processor is configured to execute instructions to
collect data including test substrate data and fault detection data
for maintenance recovery of at least one manufacturing tool in a
manufacturing facility, determine a relationship between tool
parameter settings for the at least one manufacturing tool and at
least some collected data including the test substrate data. The
method further includes utilizing zero or more virtual metrology
predictive algorithms and at least some collected data to obtain a
metrology prediction and applying multivariate run-to-run (R2R)
control modeling to obtain a state estimation including a current
operating region of the at least one manufacturing tool based on
the test substrate data and obtain at least one tool parameter
adjustment for at least one target parameter for the at least one
manufacturing tool. In one example, the R2R control modeling
utilizes the following parameters: sensor data obtained from a
sensor of the at least manufacturing tool, state at time k, state
at time k+1, sensor noise, metrology measurement noise, metrology
measurement at time k, a state transition matrix, a process
sensitivity matrix, and an observation model matrix.
[0072] In one example, the virtual metrology predictive algorithm
is tuned prior to or during its use in a tool parameter adjustment
event of the at least one tool parameter adjustment. In one
embodiment, the collected data includes a thickness profile and a
dopant concentration for maintenance recovery of a deposition tool.
The tool parameter adjustments include adjusting a temperature
parameter, lamp power ratios, and gas flow parameters for the
deposition tool.
[0073] In one example, applying multivariate run-to-run (R2R)
control modeling to obtain tool parameter adjustments for the at
least one manufacturing tool occurs after maintenance to reduce
maintenance recovery time and to reduce requalification time.
[0074] It is to be understood that the above description is
intended to be illustrative, and not restrictive. Many other
embodiments will be apparent to those of skill in the art upon
reading and understanding the above description. Although the
present invention has been described with reference to specific
exemplary embodiments, it will be recognized that the invention is
not limited to the embodiments described, but can be practiced with
modification and alteration within the spirit and scope of the
appended claims. Accordingly, the specification and drawings are to
be regarded in an illustrative sense rather than a restrictive
sense. The scope of the invention should, therefore, be determined
with reference to the appended claims, along with the full scope of
equivalents to which such claims are entitled.
* * * * *