U.S. patent application number 11/458637 was filed with the patent office on 2006-11-09 for method and system for correlating and combining production and non-production data for analysis.
This patent application is currently assigned to Micron Technology, Inc.. Invention is credited to Yuko Maeda, Shinichi Murakami, Naoki Toyoshima.
Application Number | 20060250906 11/458637 |
Document ID | / |
Family ID | 34861810 |
Filed Date | 2006-11-09 |
United States Patent
Application |
20060250906 |
Kind Code |
A1 |
Toyoshima; Naoki ; et
al. |
November 9, 2006 |
METHOD AND SYSTEM FOR CORRELATING AND COMBINING PRODUCTION AND
NON-PRODUCTION DATA FOR ANALYSIS
Abstract
This document discusses, among other things, a method and system
for correlating and combining production and non-production data
for analysis for the purposes of increasing manufacturing
efficiency and reducing manufacturing downtime due to abnormal
conditions. In one example, this method provides for quicker data
analysis which may result in less manufacturing product being
discarded due to lengthy delays between abnormal conditions and the
response to those conditions. In one example, a computer system is
used to implement the method with the data captured from production
and non-production sources being stored remotely on a server. In
one example, a computer system is used to implement the method with
the analyzed data being stored remotely on a server and accessed
over a network for local examination.
Inventors: |
Toyoshima; Naoki; (Hyogo,
JP) ; Murakami; Shinichi; (Akashi Hyogo, JP) ;
Maeda; Yuko; (Takarazuka, JP) |
Correspondence
Address: |
SCHWEGMAN, LUNDBERG, WOESSNER & KLUTH, P.A.
P.O. BOX 2938
MINNEAPOLIS
MN
55402
US
|
Assignee: |
Micron Technology, Inc.
|
Family ID: |
34861810 |
Appl. No.: |
11/458637 |
Filed: |
July 19, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10786678 |
Feb 25, 2004 |
|
|
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11458637 |
Jul 19, 2006 |
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Current U.S.
Class: |
369/1 |
Current CPC
Class: |
G06Q 10/06 20130101 |
Class at
Publication: |
369/001 |
International
Class: |
H04B 1/20 20060101
H04B001/20 |
Claims
1. A computer system, comprising: a processor; at least one input
device; at least one output device; at least one communications
interface device; a storage device containing instructions for
performing a method, the method comprising: collecting production
data; collecting non-production data; performing calculations on
the production data; performing calculations on the non-production
data; keying production data; keying non-production data; combining
the production data and the non-production data into a single data
set; analyzing said data set; and examining the analysis of the
data; and a bus connecting the processor, input device, output
device and storage device.
2. The computer system of claim 1, wherein collecting production
data includes collecting production data from a test probe.
3. The computer system of claim 1, wherein collecting production
data includes collecting parametric production data.
4. The computer system of claim 1, wherein collecting production
data includes collecting data on film thickness.
5. The computer system of claim 1, wherein collecting production
data includes collecting data on critical dimensions.
6. The computer system of claim 1, wherein collecting production
data includes any other data that is relevant to the production
process and its condition.
7. The computer system of claim 1, wherein collecting
non-production data includes collecting non-production data from a
single data source at a single source location.
8. The computer system of claim 1, wherein collecting
non-production data includes collecting non-production data from a
single data source from a plurality of locations.
9. A computer system, comprising: a processor; at least one input
device; at least one output device; at least one communications
interface device; a storage device containing instructions for
performing a method, the method comprising: collecting production
data; collecting non-production data from a single data source with
some temporal periodicity; performing calculations on the
production data; performing calculations on the non-production
data; keying the production data; keying the non-production data;
combining the production data and the non-production data into a
single data set; analyzing the single data set; and examining the
analysis of the data; and a bus connecting the processor, input
device, output device, communications interface device and storage
device.
10. The computer system of claim 9, wherein the temporal
periodicity is fixed.
11. The computer system of claim 9, wherein the temporal
periodicity is not fixed.
12. The computer system of claim 9, wherein collecting
non-production data includes collecting atmospheric data.
13. The computer system of claim 9, wherein collecting
non-production data includes collecting facility related quality
data.
14. The computer system of claim 9, wherein collecting
non-production data includes collecting equipment control data.
15. The computer system of claim 9, wherein collecting
non-production data includes collecting metrology tool calibration
data.
16. The computer system of claim 9, wherein collecting
non-production data includes collecting any other data relevant to
the production environment.
17. A computer system, comprising: a processor; at least one input
device; at least one output device; at least one communications
interface device; a storage device containing instructions for
performing a method, the method comprising: collecting production
data; collecting non-production data; performing calculations on
the production data; performing weighted mean calculations on the
non-production data; keying the production data; keying the
non-production data; combining the production data and the
non-production data into a single data set; analyzing the single
data set; and examining the analysis of the data; and a bus
connecting the processor, input device, output device,
communications interface device and storage device.
18. The computer system of claim 17, wherein the weighted mean
calculation is weighted first by location where the data sources
are from a plurality of locations, given by the following equation:
V = n = 1 i .times. [ d i n = 1 i .times. d i ] .times. S i
##EQU5## where, V is the calculated data point, d.sub.i is the
distance between the sampling point and the process location and
S.sub.i is the data being measured at the sampling point.
19. The computer system of claim 18, wherein the data is further
calculated by performing a weighted mean calculation by time, given
by the equation: V = ( 1 tS x - 1 - tS x ) .function. [ S x
.function. ( tS x + 1 - tL v ) + S x + 1 .function. ( tL v - tS x )
] ##EQU6## where V is the calculated lot data to be keyed to the
production lot data, tS.sub.x is the time of the most recent
facility data sampling, tS.sub.X+1 is the time of the next
consecutive facility data sampling, and tL.sub.v is the time of
processing the production lot.
20. The computer system of claim 17, wherein the weighted mean
calculation is weighted by time, given by the equation: V = ( 1 tS
x - 1 - tS x ) .function. [ S x .function. ( tS x + 1 - tL v ) + S
x + 1 .function. ( tL v - tS x ) ] ##EQU7## where V is the
calculated lot data to be keyed to the production lot data,
tS.sub.x is the time of the most recent facility data sampling,
tS.sub.X+1 is the time of the next consecutive facility data
sampling, and tL.sub.v is the time of processing the production
lot.
21. The computer system of claim 17, wherein keying the production
data includes adding the calculated non-production data to the
appropriate production data.
22. The computer system of claim 21, wherein the appropriate
production data is data from production lots that were processed
during the collection of relevant non-production data.
23. A computer system, comprising: a processor; at least one input
device; at least one output device; at least one communications
interface device; a storage device containing instructions for
performing a method, the method comprising: collecting production
data; collecting non-production data; performing calculations on
the production data; performing weighted mean calculations on the
non-production data; keying the production data; keying the
non-production data; identifying points of data commonality between
the production and non-production data set; defining relationships
based on the identified commonalities; combining the production
data and the non-production data based on the defined relationships
into a single data-set; analyzing the single data-set; and
examining the analysis of the data; and a bus connecting the
processor, input device, output device, communications interface
device and storage device.
24. The computer system of claim 23, wherein analyzing said single
data set includes performing a trend analysis.
25. The computer system of claim 23, wherein analyzing said single
data set includes statistical analysis.
26. The computer system of claim 23, wherein examining analysis of
the data includes comparing the analysis of the collected data to
some baseline analysis and identifying areas where trends are out
of specifications.
27. The computer system of claim 23, wherein examining analysis of
the data includes comparing the analysis of the collected data to
some baseline analysis and identifying areas where readings are out
of specifications.
28. The computer system of claim 23, wherein examining analysis of
the data includes comparing the analysis of the collected data to
some baseline analysis and identifying areas where trends and
readings are out of specifications.
29. A computer system, comprising: a processor; at least one input
device; at least one output device; at least one communications
interface device; a storage device containing instructions for
performing a method, the method comprising: collecting production
data; collecting non-production data; performing calculations on
the production data; performing weighted mean calculations on the
non-production data; keying the production data; keying the
non-production data; identifying points of data commonality between
the production and non-production data set; defining relationships
based on the identified commonalities; combining the production
data and the non-production data based on the defined relationships
into a single data-set; analyzing a single data-set stored remotely
on a server; and examining the analysis of the data; and a bus
connecting the processor, input device, output device,
communications interface device and storage device.
30. The computer system of claim 29, wherein analyzing said single
data set includes performing a trend analysis.
31. The computer system of claim 29, wherein analyzing said single
data set includes statistical analysis.
32. The computer system of claim 29, wherein examining analysis of
the data includes comparing the analysis of the collected data to
some baseline analysis and identifying areas where trends are out
of specifications.
33. The computer system of claim 29, wherein examining analysis of
the data includes comparing the analysis of the collected data to
some baseline analysis and identifying areas where readings are out
of specifications.
34. The computer system of claim 29, wherein examining analysis of
the data includes comparing the analysis of the collected data to
some baseline analysis and identifying areas where trends and
readings are out of specifications.
35. A computer system, comprising: a processor; at least one input
device; at least one output device; at least one communications
interface device; a storage device containing instructions for
performing a method, the method comprising: collecting production
data; collecting non-production data; performing calculations on
the production data; performing calculations on the non-production
data; keying production data; keying non-production data; combining
the production data and the non-production data into a single data
set; analyzing said data set; examining the analysis of the data;
and responding to the examination of the analysis; and a bus
connecting the processor, input device, output device,
communications interface device and storage device.
36. The computer system of claim 35, wherein the responding
includes an alert message displayed on the output device when the
examination detects a trend in the data that is outside of expected
results.
37. The computer system of claim 35, wherein the responding
includes an alert message displayed on the output device when the
examination detects a data reading that is outside of expected
results.
38. The computer system of claim 35, wherein the responding
includes an alert message displayed on the output device when the
examination detects a trend in the data and a reading in the data
that is outside of expected results.
39. The computer system of claim 35, wherein the responding
includes non-manually halting the manufacturing process when the
examination detects a trend in the data that is outside of expected
results.
40. The computer system of claim 35, wherein the responding
includes non-manually halting the manufacturing process when the
examination detects a reading in the data that is outside of
expected results.
41. The computer system of claim 35, wherein the responding
includes non-manually halting the manufacturing process when the
examination detects a trend in the data and a reading in the data
that is outside of expected results.
42. A method of responding to out of specification conditions in
electronic device manufacturing, comprising: collecting production
data from at least one of a plurality of data sources; collecting
non-production data from the of plurality of data sources separated
by some non-fixed distance from a manufacturing process; performing
calculations on the production data; performing weighted mean
calculations on the non-production data, weighted by time, distance
or distance/time; keying production data by adding the of a
plurality of calculated production data to the production data from
the production lots that were processed during the collection of
the non-production data; combining the production data and the
non-production data into a single data set; analyzing said data
set; and examining the analysis of the data. combining the
production data and the non-production data based on the defined
relationships into a single data-set; analyzing the single data-set
by trend or statistical analysis; examining the analysis of the
data for the occurrence of readings or trends that are out of
specifications; and responding to the examination of the
analysis.
43. The method of claim 42, wherein responding to the examination
of the analysis includes stopping the manufacturing process where
the examination detects out of specification readings or
trends.
44. The method of claim 42, wherein responding to the examination
of the analysis includes continuing production where the
examination detects no out of specification readings or trends.
45. The method of claim 42, wherein the analyzing the single
data-set includes analyzing a single data set remotely stored on a
server
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Continuation of U.S. application Ser.
No. 10/786,678, filed on Feb. 25, 2004, which is incorporated
herein by reference.
[0002] This patent application is related to U.S. patent
application Ser. No. 10/789,895 entitled METHOD AND SYSTEM FOR
AGGREGATING AND COMBINING MANUFACTURING DATA FOR ANALYSIS, assigned
to Micron Technology, Inc., and incorporated herein by
reference.
TECHNICAL FIELD
[0003] The present invention generally relates to integrated
circuit manufacturing. The present invention also generally relates
to methods for reducing integrated circuit manufacturing
abnormalities. The present invention also generally relates to a
method to correlate production data and non-production data from an
integrated circuit manufacturing process for data analysis.
BACKGROUND
[0004] The current rapid progress of design rules and processing
technology in semiconductor manufacturing makes yield and
characteristics analysis more and more difficult and complicated.
Typically production data is stored in one database, and
non-production data is stored in another database. Data analysis is
performed on the production data, using a variety of points for
comparison. This data analysis may show abnormalities in production
trends. Non-production data is separately gathered and subjected to
separate analysis of conditions and identification of significant
trends.
[0005] This data analysis is important to the quality of the
manufactured material. Variations in production conditions can
cause entire lots of product to be discarded, wasting valuable
production time and money. Quick data analysis may avoid wholesale
scrapping of product. Unfortunately the large magnitude of data
that is collected hinders a quick analysis that would be meaningful
to production goals.
[0006] Further compounding the analysis problem is that factors
typically thought to be non-production are not considered in the
analysis. Environmental measurements which can greatly affect the
quality of manufacturing end-product are just one example of these
factors. Even when one is able to qualitatively measure these
factors, connecting that meaningfully to other measurements
considered to be non-production for the purposes of data analysis
requires a user to manually examine the data for commonalities and
correlate the data based on those. Further combining that
combination with actual production data greatly compounds the
amount of data as well as compounding the inability to perform
meaningful and timely data analysis.
[0007] What is needed is a technique to quickly combine data from
production and non-production sources into a combined set of data
for quicker analysis.
Commonly Assigned Patents on Manufacturing Process Data
measurement
[0008] The following patents are commonly assigned to the assignee
of the current application and are exemplary of the types of
measuring devices and data that could be used in an integrated
circuit manufacturing facility and could be combined by an
embodiment of the present invention to provide for quicker data
analysis and manufacturing decisions. The documents listed herein
are incorporated by reference for any purpose.
[0009] U.S. Pat. No. 6,256,593, "System for Evaluating and
Reporting Semiconductor Test Processes;"
[0010] U.S. Pat. No. 6,427,092. "Method for Continuous Non
Lot-Based Integrated Circuit Manufacturing;"
[0011] U.S. Pat. No. 6,446,017, "Method and System for Tracking
Manufacturing Data for Integrated Circuit Parts;"
[0012] U.S. Pat. No. 6,534,785, "Reduced Terminal Testing
System;"
[0013] U.S. Pat. No. 6,594,013, "Reflectance Method for Evaluating
the Surface Characteristics of Opaque Materials;"
[0014] U.S. Pat. No. 6,605,159, "Device and Method for Collecting
and Measuring Chemical Samples on Pad Surface Chip;"
[0015] U.S. Pat. No. 6,622,102, "Method and System for Tracking
Manufacturing Data for Integrated Circuit Parts;" and
[0016] U.S. Pat. No. 6,628,410, "Endpoint Detector and Method for
Measuring a Change in Wafer Thickness in Chemical-Mechanical
Polishing of Semiconductor Wafers and Other Microelectronic
Substrates;"
BRIEF DESCRIPTION OF THE FIGS.
[0017] FIG. 1 is a pictorial representation of an exemplary
manufacturing facility with a manufacturing process contained
therein.
[0018] FIG. 2 is a flowchart illustrating generally, among other
things, a method for collecting and correlating production and
non-production data for analysis according to an embodiment of the
present invention.
[0019] FIG. 3 is a pictorial representation of a scenario of an
embodiment of the present invention.
[0020] FIG. 4 is a pictorial representation of a scenario of an
embodiment of the present invention.
[0021] FIG. 5 is a flowchart illustrating generally, among other
things, a method for collecting and correlating production and
non-production data for analysis according to an embodiment of the
present invention.
[0022] FIG. 6A is a pictorial representation of a vertical furnace
operation according to an embodiment of the present invention.
[0023] FIG. 6B is a pictorial representation of a vertical furnace
operation according to an embodiment of the present invention.
[0024] FIG. 7 is a block diagram illustrating generally, among
other things, one example of portions of a data analysis system,
and an environment with which it is used, for processing and
analyzing production and non-production data.
DETAILED DESCRIPTION OF THE INVENTION
[0025] In the following detailed description of exemplary
embodiments of the invention, reference is made to the accompanying
drawings (where like numbers represent like elements), which form a
part hereof, and in which is shown by way of illustration specific
exemplary embodiments in which the invention may be practices.
Those embodiments are described in sufficient detail to enable
those skilled in the art to practice the invention, and it is to be
understood that other embodiments may be utilized and logical,
mechanical, electrical and other changes may be made without
departing from the scope of the present invention.
[0026] In the description, numerous specific details are set forth
to provide a thorough understanding of the invention. However, it
is understood that the invention may be practiced without those
specific details. In other instances, well-known circuits,
structures and techniques have not been shown in detail in order
not to obscure the invention.
[0027] Parts of the description may be presented in terms of
operations performed through the execution of programming
instructions. As well understood by those skilled in the art, those
operations may take the form of electrical, magnetic, or optical
signals capable of being stored, transferred, combined, and
otherwise manipulated through, for example, electrical
components.
[0028] The term "horizontal" as used in this application is defined
as a plane parallel to the conventional plane or surface of a wafer
or substrate, regardless of the orientation of the wafer or
substrate. The term "vertical" refers to a direction perpendicular
to the horizontal as defined above. Prepositions, such as "on",
"side" (as in "sidewall"), "higher", "above", "lower", "over",
"below", and "under" are defined with respect to the conventional
plane or surface being on the top surface of the wafer or
substrate, regardless of the orientation of the wafer or substrate.
The following detailed description is, therefore, not to be taken
in a limiting sense, and the scope of the present invention is
defined only by the appended claims, along with the full scope of
equivalents to which such claims are entitled.
[0029] FIG. 1 depicts a pictorial representation of a simplified
manufacturing process for items. Items, in an embodiment, include
integrated circuits. The item undergoing processing 101 enters the
process 110 and exits as a finished product 102. The process 110 is
located in a larger manufacturing facility 120. Conditions in the
machine performing the process are very important to the quality of
the end product 102. The conditions of the item undergoing
processing 103 are also very important to the quality of the end
product. In addition, the conditions of the manufacturing facility
120 may also impact the quality of the end product. Measurements
may be taken on the item 101, 102, and 103, as well as conditions
of the actual manufacturing process 110. These measurements can be
called production data. The production data is from sources that
are directly related to the manufacturing process being performed.
These sources include, but are not limited to, test probe data,
parametric data, film thickness data, and critical dimension data.
In an embodiment, a particular production data sample is gathered
once per lot, i.e. production lot data. A production lot can be
defined as a subset of the entirety of manufactured items, for
example a plurality of work pieces such as electronic devices,
integrated circuits, substrates, semiconductor wafers, or other
similar structures in this art. A lot may further be considered as
that quantity of product produced under similar conditions, at a
similar establishment, over some period of time. In an embodiment,
a particular production data sample is gathered multiple times per
lot. In an embodiment, a particular data sample is applied across
multiple production lots. Though this detailed description uses the
term production data to refer to these data measurements, this is
not to be taken in a limiting sense, as any data that relates
directly to the manufacturing process being performed is considered
to be production data, regardless of what it is actually called.
Further, production data may be further defined as being either
online or offline. Online data may be data which is measured
directly on the item being manufactured and may be things such as
the temperature of the manufactured item, or its thickness. Online
data may also be data measured from the manufacturing process in
question while the item is being processed. Offline data is that
data that, though directly related to the manufacturing process, is
not measured on the actual manufactured item or during the actual
manufacturing step, such as the operating temperature of the
machine, the operating pressure, or some other measurement.
[0030] The pictorial element labeled 120 represents the entire
facility in which the manufacturing process resides. Measurements
may be conducted on the entire facility, as well. These
measurements can be called non-production data or alternatively,
facility data. The non-production data is from sources not directly
related to the manufacturing process. These sources include, but
are not limited to, atmospheric conditions, water conditions, gas
conditions, chemical conditions, exhaust pressure, and electrical
conditions. In an embodiment, a particular sample is gathered from
one location by one sensor. In an embodiment, a particular sample
is gathered from multiple locations by multiple sensors.
Alternatively, these measurements may be called facility data as
they generally, but without limitation, relate to the facility in
which the production takes place. Though this detailed description
uses the term non-production data, or facility data, to refer to
these data measurements, this is not to be taken in a limiting
sense, as any data that does not relate directly to the
manufacturing process can be considered to be non-production data,
or facility data, regardless of what it is actually called. This
data is inputted into a data processor 130 for further
analysis.
[0031] FIG. 2 presents a method, according to an embodiment of the
present invention, for combining the data taken during the
manufacturing process for the purposes of data analysis. Data is
collected from both production and non-production sources, at 201,
202, 203. Non-production sources may include, but not be limited
to, thermometers providing temperature readings 201a, barometers
providing pressure measurements 201c, staff productivity recording
systems, electrical measurements 201b, equipment control data,
metrology tool calibration data, etc. Non-production sources also
include sources that provide any other data relevant to the
production environment, where the production environment may
include, but not be limited to, the facility where the production
is performed, a larger physical gathering of multiple production
facilities in a single geographical location, etc. Production
sources may further be divided into online production sources and
offline production sources. Online production sources may include,
but not be limited to, critical dimension readings 202a, chemical
sampling 202b, surface readings, 202c, etc. Offline production
sources may include, but not be limited to, offline circuit testing
203a, photomask inspection 203b, operating temperature readings
203c, etc. At 204 and 205 the non-production data and the
production data are keyed to some value, respectively. In an
embodiment, the data is keyed to a temporal, or date-time, value.
In an embodiment, at 204, that data is keyed with a date-time
value. In an embodiment, at 205, that data is keyed with production
lot data, which may be further keyed with date-time values
associated with the time that a particular production lot began a
manufacturing process as well as the time that the particular
production lot completed the manufacturing process. In an
embodiment, non-production data is measured at a single location,
such as depicted in FIG. 3. In an embodiment, the sampling location
for the data measurement 310 is separated some distance 320 from
the process equipment 301. In an embodiment, production data is
measured at a plurality of locations, such as depicted in FIG. 4.
The process equipment 301 is separated by a variable distance from
the locations where data is being measured. For sampling location 1
at 310, the distance 320 can be defined as d.sub.1. For sampling
location 2 at 410, the distance 420 can be defined as d.sub.2.
There may be many locations where the data is being measured. For
all sampling locations I at 411, the distance 421 can be defined as
d.sub.i. Such multiple sampling locations 310, 410, 411 allow
statistical operations on a similar type of sampled data from a
plurality of different sample locations. For example, the different
sample locations allow a similar type of data to be collected at a
plurality of locations in a lot.
[0032] FIG. 2 further represents an embodiment of the present
invention where the data being measured, both production (including
online and offline) and non-production, are being measured at
single locations and then being keyed at 204 and 205. For some
types of data being sampled, this may be sufficient as in the case
of a single measuring device measuring a single data point that can
be used without manipulation. This might include, but not be
limited to, a thermometer measuring the air temperature of the
entire manufacturing facility, a barometer measuring the air
pressure of the entire manufacturing facility, a thickness
measuring device measuring the thickness of an exemplary wafer
sample, staff productivity measurements such as the number of
personnel on shift, etc.
[0033] FIG. 5 is substantially similar to FIG. 2 and presents at a
high level a method for combining production (including online and
offline) and non-production, with the addition of steps at 206 and
207 for performing calculations on the offline data from both
production and non-production sources. Considering the problem of
faster data analysis, it is advantageous to calculate a single data
point for a single data source. If a single data point is not
found, then all data points for that data source would need to be
added to the analysis of the data at 220. This would represent a
computational cost, both in resources in time that may hinder
quicker, more useful data analysis.
[0034] In an embodiment, at 206 and 207, the calculation being
performed is one of weighting the data. This weighting calculation
may be weighted on any number of criteria, including but not being
limited to, time, distance, production lots, operators, etc. In the
case of a physical distance weighting, the equation can be given
as: V = n = 1 i .times. [ d i n = 1 i .times. d i ] .times. S i
##EQU1##
[0035] Where, V is the calculated data point, d.sub.i is the
distance between the sampling point and the process location and
S.sub.i is the data being measured at the sampling point. This
process of weighting by location may be performed on both
production and non-production data, as some production data
measurements may be separated by some non-fixed distance from the
lot undergoing processing as some manufacturing processes are large
in size.
[0036] In the case of measurements being taken over time,
measurements of data points that are closer in time are more
relevant to our analysis. Such data points need to weighted based
on this time value. Table A represents a progression of production
lots being processed, where data samples are being taken. In an
embodiment, the samples are being taken at a single location at
that time. In an embodiment, the samples are being taken at a
plurality of locations at that substantially similar time. In such
a case, a weighted mean calculation being weighted by location
should be performed first. In Table A the first sampling is Sample
1 and as production lots undergo processing, subsequent
measurements are taken, such as Sample 2, Sample 3, Sample 4, etc.
In an embodiment, the sampling takes place at a fixed frequency
such that a non-variable number of production lots are processed
between measurements. In an embodiment, the sampling takes place at
a non-fixed frequency such that a variable number of production
lots are processed between measurements, as depicted in Table A.
The calculated data point is calculated by first determining the
time difference between the sample taken some time after the lot
was processed and the most recent measurement taken prior to the
lot beginning processing. Then weighting the sample point at each
sample time by the time difference between when the actual
production lot was processed and the sample point, as shown by the
exemplary equations in Table A in the Calculated Sample. A
generalized equation for weighting by time over a variable time
period with a variable number of lots undergoing processing between
measurements can be expressed as: V = ( 1 tS x - 1 - tS x )
.function. [ S x .function. ( tS x + 1 - tL v ) + S x + 1
.function. ( tL v - tS x ) ] ##EQU2##
[0037] where V is the calculated lot data to be keyed to the
production lot data, tS.sub.x is the time of the most recent
facility data sampling, tS.sub.X+1 is the time of the next
consecutive facility data sampling, and tL.sub.v is the time of
processing the production lot. This weighting by time can be
applied to both production data and non-production data.
TABLE-US-00001 TABLE A Time of Sampled Calculated Sampled Value
assigned Process Event Value to Production Lot Sample 1 tS1 S1 Lot
1 tL1 L1 = 1/(tS2 - tS1){S1(tS2 - tL1) + S2(tL1 - tS1)} Lot 2 tL2
Lot-.sub.i tL.sub.i L1 = 1/(tS2 - tS1){S1(tS2 - tL.sub.i) +
S2(tL.sub.i - tS1)} Lot-.sub.i+1 tL.sub.i+1 Lot-.sub.i+2 tL.sub.i+2
Sample 2 tS2 S2 Lot-.sub.j tL.sub.j L.sub.j L.sub.j = 1/(tS3 -
tS2){S2(tS3 - tL.sub.j) + S3(tL.sub.j - tS2)} Lot-.sub.j+1
Lot-.sub.j+2 Lot-.sub.j+3 Lot-.sub.j+4 Lot-.sub.j+5 Lot-.sub.j+6
Sample 3 tS3 S3 Lot-.sub.k tL.sub.k L.sub.k L.sub.k = 1/(tS4 -
tS3){S3(tS4 - tL.sub.k) + S3(tL.sub.k - tS3)} Lot-.sub.k+1
Lot-.sub.k+2 Lot-.sub.k+3 Lot-.sub.k+4 Lot-.sub.k+5 Sample 4 Ts4 S4
. . . . . . . . . . . .
[0038] In table A, tS.sub.1,2,3, . . . is defined as the time when
a data sample is taken. In table A, tL.sub.i,j,k, . . . is defined
as the time when a production lot process is begun. In table A,
-S.sub.1,2,3, . . . is defined as the data sampled. In table A,
L.sub.i,j,k, . . . is defined as the lot data calculated. In the
present invention the calculation performed to arrive at
L.sub.i,j,k, . . . is a weighted mean calculation.
[0039] Integration of equipment data as another type of facility
data presents an additional problem not addressed by the above
equations. In an embodiment, this equipment data is tool
qualification data. Data in respect to equipment control is sampled
during periodic tool qualification. In an embodiment, the period of
tool qualification is daily. This information is stored with an
associated time-stamp and the equipment ID's as well as facility
data. In an embodiment, this facility data is as described herein.
In an embodiment, the equipment data is gathered with production
data in the same way that facility data is gathered with production
data. In some cases, equipment data can not be used due to
maintenance that may reset the equipment condition. In an
embodiment, a weighted mean calculation is used to calculate the
equipment data value to be assigned to a particular lot. In an
embodiment, the weighted mean calculation is weighted by time. In
an embodiment, the weighted mean calculation is weighted by some
value other then time.
[0040] FIG. 6A and FIG. 6B depict vertical furnace operations.
Vertical furnace operations are part of process 110 in an
embodiment. In a vertical furnace operation a pilot wafer 601 is
used to monitor film thickness. It would be useful if this pilot
wafer data could be gathered and stored with production data. Pilot
wafers 601 may be used between single lots of production wafers
610, between multiple lots of production wafers 610. If a pilot
wafer 601 is inserted into the space between lots, as depicted in
FIG. 6A, the calculation of the lot data can be given by the
following calculation: V i = ( P i + P i + 1 ) 2 ##EQU3## where,
V.sub.i is the calculated pilot wafer data, P.sub.i is the measured
pilot wafer data and P.sub.i+1 is the next measured pilot wafer
data. If a pilot wafer 601 is placed at the top, center and bottom
of all processed lots, as depicted in FIG. 6B, the lot data can be
given by the following equations: V 1 = P 1 + P 2 2 ##EQU4## V 2 =
P 1 + P 2 2 ##EQU4.2## V 3 = P 2 ##EQU4.3## V 4 = P 2 + P 3 2
##EQU4.4## V 5 = P 2 + P 3 2 ##EQU4.5## where,
V.sub.1.about.V.sub.5 are the calculated pilot wafer data and
P.sub.1.about.P.sub.3 are the measured pilot wafer data. Pilot
wafers 601 may also be used at some other interval to determine
film thickness. In an embodiment, an average of pilot wafer
readings before and after processing of a single lot is completed
is used. In an embodiment, the first pilot wafer reading made some
time after the completion of the processing of the production wafer
lots is averaged with the first pilot wafer reading made some time
before the processing of the production wafer lot is begun. In an
embodiment, the pilot wafer reading made during the processing of
lot is used as the pilot wafer data for that production wafer
lot.
[0041] In an embodiment of the present invention the data collected
from the various production and non-production sources is
correlated, with reference to FIGS. 2 and 5. At 210 this
correlation is performed non-manually using at least one point of
data commonality. The points of data commonality may be amongst any
number of items, including, but not limited to, production lot
ID's, date-time values, locational, etc. In an embodiment, it is
advantageous to provide a date-time value to all the data being
calculated as it provides an easy reference from which to
non-manually related the production and non-production data. Data
related to the actual production lot is keyed with the time that a
particular lot passed through a particular process. The production
and non-production data that is gathered during the times where
that lot is being processed by a particular process are weighted by
location and then by time to determine a data point that is most
related to the specific production lot and the specific production
process that lot was going through that period.
[0042] By collecting data points that are easily related to each
other by non-manual means, a system can quickly relate the data,
and then analyze that data, providing the manufacturing operator
with indicators as to the wellness of the manufacturing operation.
Such a system is described in further detail below and in FIG. 7.
In an embodiment, a system is used to monitor each individual
manufacturing process. In an embodiment, a system is used to
monitor multiple manufacturing processes. In an embodiment, the
manufacturing operator monitors the readings of the system and
determines whether an out of specification condition exists and can
manually stop the manufacturing operation for further
investigation. In an embodiment, the manufacturing operator
responds to system generated messages of out of specification
conditions to manually stop the manufacturing operation for further
investigation. In an embodiment, the system determines, without any
operator intervention, whether an out of specification condition
exists and non-manually ceases the particular manufacturing process
under consideration, without any operator intervention. In an
embodiment, a manufacturing operator responds to conditions that
are within specifications by allowing the manufacturing process to
continue. In an embodiment, a system responds to conditions that
are within specifications by non-manually allowing the
manufacturing process to continue, without any operator
intervention.
[0043] FIG. 7 depicts a block diagram of a system for implementing
an embodiment of the invention analogous to the data processor 130
shown in FIG. 1. Illustrated are a server 701 connected to a
computer 702 via a network 710. Although one server 701, one
computer 702, and one network 710 are shown, in other embodiments
any number or combination of them may be present. Although the
server 701 and the network 710 are shown, in another embodiment
they may not be present.
[0044] The computer 702 may include a processor 730, a storage
device 740, a communications interface device 711, an output device
750, and an input device 760, all connected via a bus 770.
[0045] The processor 730 may represent a central processing unit of
any type of architecture, such as a CISC (Complex Instruction Set
Computing), RISC (Reduced Instruction Set Computing), VLIW (Very
Long Instruction Word), or a hybrid architecture, although any
appropriate processor may be used. The processor 730 may execute
instructions and may include that portion of the computer 702 that
controls the operation of the entire computer. Although not
depicted in FIG. 7, the processor 730 typically includes a control
unit that organizes data and program storage in memory and
transfers data and other information between the various parts of
the computer 702. The processor 730 may receive data from the input
device 760, may read and store code and data in the storage device
740, may send data to the output device 750, and may send and
receive code and/or data to/from the network 710.
[0046] Although the computer 702 is shown to contain only a single
processor 730 and a single bus 770, the present invention applies
equally to computers that may have multiple processors and to
computers that may have multiple buses with some or all performing
different functions in different ways. Although the computer 702 is
shown to contain only a single communications interface device 711,
the present invention applies equally to computers that may have
multiple communications interface devices with some or all
performing difference functions in different ways.
[0047] The storage device 740 represents one or more mechanisms for
storing data. For example, the storage device 740 may include read
only memory (ROM), random access memory (RAM), magnetic disk
storage media, optical storage media, flash memory devices, and/or
other machine-readable media. In other embodiments, any appropriate
type of storage device may be used. Although only one storage
device 740 is shown, multiple storage devices and multiple types of
storage devices may be present. Further, although the computer 702
is drawn to contain the storage device 740, it may be distributed
across other computers, for example on server 701.
[0048] The storage device 740 includes a controller 745, which in
an embodiment may include instructions capable of being executed on
the processor 730 to carry out the functions of the present
invention. In another embodiment, some or all of the functions of
the present invention may be carried out via hardware in lieu of a
processor-based system. Although the controller 745 is shown to be
contained within the storage device 740 in the computer 702, some
or all of the controller 745 may be distributed across other
systems, for example on the server 701 and accessed via the network
710.
[0049] The input device 760 may be a keyboard, pointing device,
mouse, trackball, touchpad, touchscreen, keypad, microphone, voice
recognition device, or any other appropriate mechanism for the user
to input data to the computer 702. Although only one input device
760 is shown, in another embodiment any number and type of input
devices may be present.
[0050] The output device 750 is that part of the computer 702 that
communicates output to the user. The output device 750 may be a
cathode-ray tube (CRT) based video display well known in the art of
computer hardware. But, in other embodiments the output device 750
may be replaced with a liquid crystal display (LCD) based or gas,
plasma-based, flat-panel display. In another embodiment, the output
device 750 may be a speaker. In still other embodiments, any
appropriate output device suitable for presenting data may be used.
Although only one output device 750 is shown, in other embodiments,
any number of output devices of different types or of the same type
may be present.
[0051] The communications interface device 711 is that part of the
computer which communicates with the network 710. The
communications interface device 711 may be a network interface card
(NIC) or modem. The NIC may include a readily available 10/100
Ethernet compatible card or a higher speed network card such as a
gigabit Ethernet or fiber optic enabled card. Other examples
include wireless network cards that operate at one or more
transmission speeds, or multiple NICs to increase the speed at
which data can be exchanged over a network 710.
[0052] The bus 770 may represent one or more busses, e.g., PCI, ISA
(Industry Standard Architecture), X-Bus, EISA (Extended Industry
Standard Architecture), or any other appropriate bus and/or bridge
(also called a bus controller).
[0053] The computer 702 may be implemented using any suitable
hardware and/or software, such as a personal computer or other
electronic computing device. Portable computers, laptop or notebook
computers, PDAs (Personal Digital Assistants), two-way alphanumeric
pagers, keypads, portable telephones, appliances with a computing
unit, pocket computers, and mainframe computers are examples of
other possible configurations of the computer 702. The hardware and
software depicted in FIG. 7 may vary for specific applications and
may include more or fewer elements than those depicted. For
example, other peripheral devices such as audio adapters, or chip
programming devices, such as EPROM (Erasable Programmable Read-Only
Memory) programming devices may be used in addition to or in place
of the hardware already depicted.
[0054] The network 710 may be any suitable network and may support
any appropriate protocol suitable for communication between the
server 701 and the computer 702. In an embodiment, the network 710
may support wireless communications. In another embodiment, the
network 710 may support hard-wired communications, such as a
telephone line or cable. In another embodiment, the network 710 may
support the Ethernet IEEE (Institute of Electrical and Electronics
Engineers) 802.3x specification. In another embodiment, the network
710 may be the Internet and may support IP (Internet Protocol). In
another embodiment, the network 710 may be a local area network
(LAN) or a wide area network (WAN). In another embodiment, the
network 710 may be a hotspot service provider network. In another
embodiment, the network 710 may be an intranet. In another
embodiment, the network 710 may be a GPRS (General Packet Radio
Service) network. In another embodiment, the network 710 may be any
appropriate cellular data network or cell-based radio network
technology. In another embodiment, the network 710 may be an IEEE
802.1 1x wireless network, where x is any alphanumeric character
used to designate a specific standard. In still another embodiment,
the network 710 may be any suitable network or combination of
networks. Although one network 710 is shown, in other embodiments
any number of networks (of the same or different types) may be
present.
[0055] In an embodiment the non-manual relation and analysis of the
data may be performed by computer code contained on the storage
device 740 and further operated on by the processor 730 of the
computer 702. In an embodiment the analysis is displayed to a user
via the output device 750.
[0056] In an embodiment the non-manual relation and analysis of the
data may be performed by the server 701 configured similarly to the
computer 702 in that it has computer code contained in a storage
device similar to the storage device 740 of the computer 702 and
that computer code is operated on by a processor similar to the
processor 730 of the computer 702.
[0057] In an embodiment the analysis is transmitted over a network
710 to a Client Interface 1 780. In an embodiment the Client
Interface 1 is physically separated from the server 702. In an
embodiment the analysis is transmitted over a network 710 and
further over a Wide Area Network 715, such as the Internet, to a
Client Interface 2 785. In an embodiment the analysis is conducted
on Client Interface 1 and the data is transmitted over the
communications network 710 for operations to be performed on Client
Interface 1 780, configured similarly to the computer 702 of FIG.
7. In an embodiment the analysis is conducted on Client Interface 2
785 and the data is transmitted over the communications network
710, and further over the Wide Area Network 785, such as the
Internet, for operations to be performed on Client Interface 2 785,
configured similarly to the computer 702 of FIG. 7.
[0058] In an embodiment, the examination of the analysis is
conducted by a manufacturing operator via a Client Interface 1 780
accessing the data remotely over a communications network 710 from
a server 702. In an embodiment, the examination of the analysis is
conducted by a manufacturing operator via a Client Interface 2 785
accessing the data remotely over a communications network 710, and
further over a Wide Area Network 715, such as the Internet, from a
server 702.
[0059] As was described in detail above, aspects of an embodiment
pertain to specific apparatus and method elements implementable on
a computer or other electronic device. In another embodiment, the
invention may be implemented as a program product for use with an
electronic device. The programs defining the functions of this
embodiment may be delivered to an electronic device via a variety
of signal-bearing media, which include, but are not limited to:
[0060] (1) information permanently stored on a non-rewriteable
storage medium, e.g., a read-only memory device attached to or
within an electronic device, such as a CD-ROM readable by a CD-ROM
drive;
[0061] (2) alterable information stored on a rewriteable storage
medium, e.g., a hard disk drive or diskette; or
[0062] (3) information conveyed to an electronic device by a
communications medium, such as through a computer or a telephone
network, including wireless communications.
[0063] Such signal-bearing media, when carrying machine-readable
instructions that direct the functions of the present invention,
represent embodiments of the present invention.
[0064] There are distinct advantages to this type of data
correlation and subsequent analysis. It allows for a single trend
chart to measure trends in the data. It also provides for an
"apples to apples" relational study and makes a correlation study
or statistical analysis simpler to achieve.
* * * * *