U.S. patent application number 11/460723 was filed with the patent office on 2008-02-07 for data mining to detect performance quality of tools used repetitively in manufacturing.
This patent application is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Jeong Woo Nam, Viorel Ontalus, Yunsheng Song.
Application Number | 20080033589 11/460723 |
Document ID | / |
Family ID | 39030277 |
Filed Date | 2008-02-07 |
United States Patent
Application |
20080033589 |
Kind Code |
A1 |
Ontalus; Viorel ; et
al. |
February 7, 2008 |
DATA MINING TO DETECT PERFORMANCE QUALITY OF TOOLS USED
REPETITIVELY IN MANUFACTURING
Abstract
A tool with one or more chambers in a manufacturing system is
identified as performing at or below an acceptable level by the
following steps: Store process data from tools for each one of a
plurality of individual processes for a processed object in a
process database; Store tool performance data for each individual
process for a processed object in a yield database; Develop
statistics for similar tool sets associating data with each of the
similar tool units; Generate yield numbers for each group of the
similar tool units based upon the statistics; and identify
poorly/well performing tool units by using the yield numbers.
Inventors: |
Ontalus; Viorel; (Danbury,
CT) ; Nam; Jeong Woo; (Poughquag, NY) ; Song;
Yunsheng; (Poughkeepsie, NY) |
Correspondence
Address: |
INTERNATIONAL BUSINESS MACHINES CORPORATION;DEPT. 18G
BLDG. 300-482, 2070 ROUTE 52
HOPEWELL JUNCTION
NY
12533
US
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION
Armonk
NY
|
Family ID: |
39030277 |
Appl. No.: |
11/460723 |
Filed: |
July 28, 2006 |
Current U.S.
Class: |
700/109 ;
700/110; 700/121 |
Current CPC
Class: |
G05B 19/41875 20130101;
G05B 2219/32196 20130101; G05B 2219/45031 20130101; Y02P 90/22
20151101; Y02P 90/02 20151101; G05B 2219/32201 20130101 |
Class at
Publication: |
700/109 ;
700/110; 700/121 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1. A method of determining the quality of performance of tool units
wherein a tool unit comprises a unit selected from the group
consisting of an individual tool and a tool chamber in a
multichamber tool in a manufacturing system, where multiple similar
tool units are provided at each of a series of tool stations used
in repetitive cycles of processing objects through a production
line until completion of manufacturing thereof by said
manufacturing system, comprising the steps as follows: processing
objects through said tool stations in said production line
repetitively with one of a plurality of tool units at each station
being allocated to process one of said objects with one of said
tool units on a random basis on each cycle of progress of said
object through said production line; storing process data including
tool identification and object identification from each of said
tool units which processes one of said objects at a said tool
station for each one of a plurality of individual processes for a
processed object in a process database, collecting functional test
data from manufactured objects at the completion of processing
thereof and passing said functional test data for each object to an
object yield data processor; calculating object yield data for each
individual ones of said objects based upon said functional test
data; data mining to develop statistics for similar tool units by
associating said object yield data with each of said similar tool
units; generating yield numbers for each group of said similar tool
units based upon said statistics; and identifying well performing
tools and poorly performing tools by using said yield numbers.
2. The method of claim 1 including generating statistical numbers
and associated yield numbers for each set of shared tools, for all
tool combinations possible, without accounting for tool
sequence.
3. The method of claim 1 including: identifying a poorly performing
tool when all combinations with that tool have a depressed yield;
and identifying a well performing tool when all combinations with
that tool have an acceptable yield.
4. The method of claim 1 including generating statistical numbers
and associated yield numbers for each tool/chamber frequency of
tool usage.
5. The method of claim 4 including calculating a yield number for
each frequency of tool usage.
6. The method of claim 5 including identifying a tool/chamber as
performing at an unacceptable level when yield decreases
monotonically with frequency of tool usage.
7. The method of claim 1 including: supplying data from said data
collection processor to a logistic data base processor; supplying
data from said logistic database processor to said data mining
processor; and employing data mining processing to determine
performance of a said tool/chamber.
8. The method of claim 1 including employing SQL to summarize data
for each object by tool and lot.
9. The method of claim 8 including creating a frequency of usage
table of data by lot, object, and tool.
10. The method of claim 9 including creating an object yield
database.
11. The method of claim 10 including forming a composite table by
joining said frequency of tool usage table and said object yield
database.
12. The method of claim 11 including taking frequency of tool usage
as an independent variable and yield as a dependent variable.
13. The method of claim 12 including determining whether tool
frequency of usage and tool yield correlate.
14. (canceled)
15. A system for identifying performance level of at least one
chamber and/or at least one stage in a manufacturing system with
respect to an acceptable level comprising: a process database for
storing process data from groups of similar tools for each one of a
plurality of individual processes for a processed object where said
individual processes are performed at each of a series of stations
by a tool unit randomly selected from one of said groups of similar
tools located a given station; an object yield database for storing
tool performance data for each individual process for a processed
object at the end of processing through said manufacturing system;
a data mining processor for developing statistics for each tool in
said groups of similar tool units by associating said object yield
data with data from said process database for each of said sets of
similar tool units; a yield analyzer which generates yield numbers
for each group of said similar tool units based upon said
statistics; and a performance analyzer which identifies well and
poorly performing tool units in said groups by using said yield
numbers.
16. The system of claim 15 wherein said data mining processor
generates statistical numbers and associated yield numbers for each
set of shared tools, for all tool combinations possible, without
accounting for tool sequence.
17. The method of claim 15 wherein said data mining processor
identifies a poorly performing tool when all combinations with that
tool have a depressed yield.
18. The method of claim 15 wherein said data mining processor
identifies a well performing tool when all combinations with that
tool have an acceptable yield.
19. The method of claim 15 wherein said data mining processor
generates statistical numbers and associated yield numbers for each
tool/chamber frequency of usage.
20. The method of claim 19 wherein said data mining processor
calculates a yield number for each frequency and identifies a
tool/chamber as performing at an unacceptable level when yield
decreases monotonically with tool usage.
Description
BACKGROUND OF THE INVENTION
[0001] This invention relates to the identification of tools or
tool chambers producing defective products in a multiple tool
manufacturing environment where those tools are reused in
successive stages of manufacture of a single product. More
particularly, the present invention relates to computer implemented
methods and systems for storing and analyzing product history and
failure data to determine which tools produce defective products
when reused in that manner.
[0002] FIG. 1A is a diagram 2 of a manufacturing process flow of
processing steps employed in the manufacture of microelectronic
devices comprising semiconductor chips. The diagram 2 illustrates a
series of cycles of processing steps starting with a set of Front
End Of Line (FEOL) block 3 of processing steps and continuing until
the end of a set of Back End Of Line (BEOL) block 4 of processing
steps. Below the FEOL block 3 and BEOL block 4 is a chart showing
the metallization process phases from the metal layer phase 1,
through to the last three metallization phases N-1, N and N+1. The
semiconductor devices are formed in the FEOL process steps and then
preferably consecutively metal layer 1, . . . metal layer N-1,
metal layer N, and through N+1 are formed in the BEOL
processing.
[0003] To simplify the description it is assumed that the
processing occurs in several cycles of consecutive sequence
processing steps which are repeated several times to form the
metallization layers required on a semiconductor device. That is to
say that several consecutive processing steps are performed
repeatedly. The processing is performed at stages each of which
includes several tools (hereinafter referring to both individual
tools and chambers in multi-chamber tools). The various stages
perform functions such as deposition of metal, deposition of
dielectric layers, patterning (forming masks), and etching by a
process such as Reactive Ion Etching (RIE). While the method of
this invention does not require repetition of the processing
sequence, the description included herein relates to repeated
cycling through the same sequence of tool stages. It will be well
understood by those skilled in the art that the sequence of
processing and the tools employed may vary considerably, and that
the present invention can be applied to such diverse sequences of
operation as well.
[0004] In many cases in the manufacture of semiconductor devices,
similar process steps are repeated several times during a product
manufacturing cycle and a common set of tools is reused for each of
such similar process steps. In order to enhance the yield of
manufacturing of semiconductor devices, it is necessary to identify
a bad tool or a tool that is under-performing, but also to identify
the best performing tools. Furthermore it is desirable to determine
which one of the chambers of a multi-chamber tool is bad, i.e.
under-performing. It is also desirable to determine which ones of
the tools (chambers) is one of the best performing units on factory
floor. Heretofore the state of the art methods employed for
detecting under-performing tools in manufacturing were based on
uni-process analysis. A common method of analysis has been to
monitor the yield for each process step individually and to
identify the tool performance based upon that data. However in a
very complex process where very large numbers of steps are
performed without the ability of being able to monitor the yield
for each process step, the problem of determining the level of
performance of each individual tool has become a difficult
problem.
[0005] Referring to FIG. 1B, a system 10 is shown for performing
data collection from a series of workpieces W being manufactured on
a factory floor 12 which includes a set of tools 12-1 to 12-4, a
Data Collection Processor (DCP) 15 and a functional test apparatus
17. The sets of tool stages 12-1 to 12-4, which include a deposit
metal stage 12-1, a deposit dielectric stage 12-2, a patterning
stage 12-3 and an RIE stage 12-4. Each of those stages 12-1 to 12-4
may include two or more similar tools which can process a given
workpiece W. The two or more similar tools at a single stage are
provided so that when one or more tools at a stage is/are otherwise
occupied with processing or in need of repair another available
tool at that stage can process the workpiece W without delay. The
workpieces W enter the deposit metal stage 12-1 on conveyor line
11A. After processing at deposit metal stage 12-1, workpiece W
moves on conveyor line 11B to dielectric deposition stage 12-2.
After processing at stage 12-2, workpiece W moves on conveyor line
11C to patterning stage 12-3. After processing at stage 12-3,
workpiece W moves on conveyor line 17A to RIE stage 12-4 where
three RIE tools A, B and C are shown to illustrate the fact that
there can be several tools at a single stage. The RIE stage 12-4
includes RIE A tool RA, RIE B tool RB, and RIE C tool RC.
[0006] The identification of workpieces W processed by individual
tools is supplied on lines 13-1 to 13-4 to the DCP 15. In
particular, each of the tools in stage 12-1 is connected to send
workpiece identification data on line 13-1 to the DCP 15. Each of
the tools in stage 12-2 is connected to send workpiece
identification data on line 13-2 to the DCP 15. Each of the tools
in stage 12-3 is connected to send workpiece identification data on
line 13-3 to the DCP 15. In stage 12-4 RIE A Tool RA is connected
by line 13-4A to send workpiece identification data to line 13-4;
RIE B Tool RB is connected to send workpiece identification data by
line 13-4B to line 13-4; and RIE C Tool RC is connected by line
13-4C to line 13-4. Lines 13-4A to 13-4C are connected to line 13-4
to send the workpiece identification data workpiece identification
data for the tools RA-RC to the DCP 15. There may also be some test
data collected which is supplied to the DCP 15, but there is no
overall test data supplied on lines 13-1 to 13-4 as to the effects
of processing by each individual tool upon the overall quality of
the workpiece W. There are other tests made by parametric testers
at various stages in the process of manufacture that provide
parametric yield data, as distinguished from functional yield test
data provided by the functional test apparatus 17 at the end of
processing.
[0007] After completion of the first cycle of processing by the
four stages 12-1 to 12-4, the workpiece W is recycled along line
14A to the input line 11 to stage 12-1 and is processed there by
whichever tool is available in stage 12-1. Then the process is
repeated at stages 12-2 to 12-4 as described above. The workpiece W
is recycled N times through stages 12-1 to 12-4 repeatedly for
manufacture of the metal layers until all of the metal layers
including the metal layer N+1 have been manufactured in accordance
with FIG. 1A. Then the workpiece W leaves the factory floor 12 on
conveyor line 14Z which delivers it for testing to the functional
test apparatus 17 which performs tests after all processing has
been completed, as will be well understood by those skilled in the
art. The data collected by the functional test apparatus is sent to
the DCP 15.
[0008] FIG. 1C is a schematic diagram which comprises a
chronological representation of several series of cycles of
repetition of consecutive metallization processing by the stages
12-1 to 12-4 on the factory floor 12 by providing a duplicate block
for each cycle of repetition of N repetitive uses of a stage in the
manufacture of workpieces W which are being manufactured and
tested.
[0009] The stage 12-1 includes a set of duplicate blocks for
processing including deposit metal cycle 1 block 12-1A, a deposit
metal cycle 2 block 12-1B therebelow; as well as deposit metal
cycle N-2 block 12-1X; deposit metal cycle N-1 block 12-1Y and
deposit metal cycle N block 12-2Z therebelow.
[0010] The stage 12-2 includes another set of duplicate blocks
including a deposit dielectric cycle 1 block 12-2A, a deposit
dielectric cycle 2 block 12-2B therebelow; a deposit dielectric
cycle 2 block 12-2B, as well as deposit dielectric cycle N-2 block
12-2X; deposit dielectric cycle N-1 block 12-2Y and deposit
dielectric cycle N block 12-2Z therebelow.
[0011] The stage 12-3 includes still another set of duplicate
blocks including a pattern cycle 1 block 12-3A, a pattern cycle 2
block 12-3B therebelow; a pattern cycle 2 block 12-3B, as well as
pattern cycle N-2 block 12-3X; pattern cycle N-1 block 12-3Y and
pattern cycle N block 12-2Z therebelow.
[0012] The stage 12-4 includes yet another set of duplicate blocks
including an RIE cycle 1 block 12-2A, an RIE cycle 2 block 12-2B
therebelow; an RIE cycle 3 block 12-2B, as well as an RIE cycle N-2
block 12-2X; an RIE cycle N-1 block 12-2Y and an RIE cycle N block
12-2Z therebelow.
[0013] At the end of the first cycle of processing in RIE stage
12-4 in FIG. 1C, the workpiece W is transported on conveyer line
14A for recycling to stage 12-1 as indicated by block 12-1B which
represents the fact that the workpiece W is now in the process of
being subjected to BEOL metal layer 2 processing and that it will
be processed consecutively by tools in the four stages 12-1 to
12-4, but that at each stage it will very likely be processed by a
different tool at each of those stages 12-1 to 12-4 from the tool
employed in the first cycle because some of the tools will be busy
with other workpieces, or otherwise unavailable at the time the
workpiece W arrives on line 14A. In the second cycle the workpiece
is processed consecutively at deposit metal stage 12-1B; deposit
dielectric stage 12-2B; pattern cycle 12-3B and one of the tools
RA-RB in RIE stage 12-4B.
[0014] At the end of the second cycle of processing in RIE stage
12-4 in FIG. 1C, the workpiece W is recycled back on conveyer line
14B to stage 12-1 for BEOL metal layer 3 processing (not shown for
convenience of illustration) and that it will be processed by tools
in the four stages 12-1 to 12-4, but that it may be processed by
different tools at each of those stages 12-1 to 12-4 from those of
the first and second cycles because some of the tools will be busy
with other workpieces, or otherwise unavailable at the time the
workpiece W arrives on line 14B. Then the workpiece W is processed
consecutively at deposit metal stage 12-1; deposit dielectric stage
12-2; pattern cycle 12-3 and one of the tools RA-RB in RIE stage
12-4 and the dots below line 14B indicate that numerous cycles of
processing are omitted for convenience of illustration.
[0015] At the end of a subsequent N-3 cycle of processing (not
shown in FIG. 1C), in RIE stage 12-4 the workpiece W is recycled
back on conveyer line 14W to stage 12-1 for BEOL metal layer N-2
processing as indicated by block 12-1X which represents the fact
that the workpiece W is now in the process of being subjected to
BEOL metal layer N-2 processing and that it will be processed
consecutively by tools in the four stages 12-1 to 12-4, but that it
will probably be processed by different tools at each of those
stages 12-1 to 12-4 from those of the some of the previous cycles
because some of the tools will be busy with other workpieces, or
otherwise unavailable at the time the workpiece W arrives on line
14W. Then the workpiece W is processed consecutively at deposit
metal stage 12-1X; deposit dielectric stage 12-2X; pattern cycle
12-3X and one of the tools RA-RB in RIE stage 12-4X.
[0016] At the end of the N-2 cycle of processing in FIG. 1C in RIE
stage 12-4, the workpiece W is recycled back on production line 14X
to stage 12-1 for BEOL metal layer N-1 processing as indicated by
block 12-1Y which represents the fact that the workpiece W is now
in the process of being subjected to BEOL metal layer N-1
processing and that it will be processed by tools in the four
stages 12-1 to 12-4, but that it will probably be processed by
different tools at each of those stages 12-1 to 12-4 from those of
many of the previous cycles because some of the tools will be busy
with other workpieces, or otherwise unavailable at the time the
workpiece W arrives on line 14X. In the N-1 cycle the workpiece W
is consecutively processed at deposit metal stage 12-1Y; deposit
dielectric stage 12-2Y; pattern cycle 12-3Y and one of the tools
RA-RB in RIE stage 12-4Y.
[0017] At the end of the N-1 cycle of processing in FIG. 1C in RIE
stage 12-4, the workpiece W is recycled back on production line 14Y
to stage 12-1 for BEOL metal layer N processing as indicated by
block 12-1Z which represents the fact that the workpiece W is now
in the process of being subjected to BEOL metal layer N processing
and that it will be processed by tools in the four stages 12-1 to
12-4, but that it will probably be processed by different tools at
each of those stages 12-1 to 12-4 from those of many of the
previous cycles because some of the tools will be busy with other
workpieces, or otherwise unavailable at the time the workpiece W
arrives on line 14Y. In the N cycle the workpiece is consecutively
processed at deposit metal stage 12-1Z; deposit dielectric stage
12-2Z; pattern cycle 12-3Z and one of the tools RA-RB in RIE stage
12-4Z.
[0018] At the end of the N cycle of processing in FIG. 1C in RIE
stage 12-4, the workpiece W is exits the factory floor back on
conveyor line 14Z which delivers it to the Functional Test
Apparatus 17 for functional testing as described above.
[0019] Referring to FIG. 2 a chart is shown of a prior art type
analysis of the yield of a process step after the repetition of a
process on a set on two tools, tool A and tool B at one stage,
where one of the two tools A and B is actually performing better
than the other. With current methods, by examination of a single
process step as shown in FIG. 2 one might conclude that the
performance of tool B is worse than that of tool A since the data
shown on the chart in FIG. 2 would indicate that to be the case.
However the problem with that approach is that the result may be
attributable to the fact that semiconductor wafers with lower
yields may have been processed on tool A at all of the other
process steps, except the step analyzed, and yet tool B would be
assumed to be the worse tool, even though that may not have been
factually correct.
[0020] Current methods of detecting under-performance are limited
in that as follows: [0021] i.) they do not account for effects
arising from repeated usage of a set of tools in subsequent process
steps, as they all relate to a single processing step without
accounting for past history; [0022] ii.) by looking at only one
process step, the analysis does not account for effects caused at
prior or subsequent use of a common tool set and could lead to
incorrect judgments regarding tool performance; [0023] iii.) small
and marginal effects are not detectable; [0024] iv.) the
under-performance of tools which perform variably over time, with
periods of acceptable performance interspersed with finite periods
of under-performance, are not detectable.
SUMMARY OF THE INVENTION
[0025] In accordance with this invention, data mining and analysis
methods are employed for the purpose of detecting the presence of
bad tool or a tool that is under-performing. The term "data mining"
as employed herein refers to the employment of a computer system to
perform an automatic search through large volumes of data to find
various patterns of defects or absence thereof. Historically
examples of data mining have included computational techniques
performed by computers including statistical analysis, machine
learning, and pattern recognition. In the context of the present
invention, data mining and analysis methods are employed to detect
the presence of bad tool or a tool that is under-performing or to
identify the best performing tools in a complex manufacturing
environment.
[0026] SQL, which is an ANSI/ISO standard, is a computer language
used to create, modify and retrieve data from relational database
management systems. The language has evolved beyond its original
purpose to support object-relational database management
systems.
[0027] The method of this invention is suited for manufacturing
where output can be described by yield and it is applicable during
a product manufacturing cycle when a similar process is repeated
several times and a common set of tools is employed. Alternative,
the method of this invention can detect the tools performing best.
The method is applicable for analysis of multi-chamber tools as
well. The method of this invention can be employed to study the
yield influence of a common set of tools in chip manufacturing, and
to detect which tools perform the worst.
[0028] The method of this invention employs an approach to analysis
which accounts for the complete history of a set of tools shared in
a sequence of many steps in a manufacturing process. The method of
this invention employs analysis of yield parameters by accumulating
and accounting for all processes that share the same tool set.
[0029] To identify a bad tool, a computer system analyzes the yield
using the combinations of tools or chamber of multi-chamber tools
applied to the manufacture of a large population of wafers, and
also the number of times wafers were processed on same
tool/chamber.
[0030] In one aspect of the method of this invention, each tool
combination is counted, without accounting for the order in which
each tool was used, and without accounting for the number of times
a tool was used and re-used. If a bad tool has been employed in the
process, all of the combinations of step in which that tool was
included will tend to have a lower yield.
[0031] FIG. 3A is a chart showing, the trend of the mean yield data
and other statistical parameters for each of several tool
combinations. In FIG. 3A for each tool combination the trend of the
mean yield data and other statistical parameters are revealed. If a
bad tool exists, the combinations containing that tool stand out
when the data are compared. In other words, in the case of a bad
tool, the data show depressed yield when compared with other tool
combinations. Referring again to the chart in FIG. 3A, a table is
formed below the chart with each column pertaining to one of nine
combinations of four tools A, B, C, and D taken, one, two, three,
or four tools at a time. Note that all of the combinations which
include tool C have lower mean yield data points as indicated by
the lowest mean yield data points with rectangles around the
circular marks around the round dots above each combination which
includes tool C. Thus it is reasonable to conclude that tool C is
bad.
[0032] In a second method illustrated by the chart in FIG. 3B, the
results are shown for an analysis of the yield for each tool which
is analyzed separately by counting the number of times the tool was
used during the process of manufacture of a given product. Then
statistical data as to mean yield is generated for each number of
usages. The Number of Observations (NOBS) vary from 0 to 1,000, in
this example, as indicated on the vertical set of values to the
right side of the chart and the HOL (Health Of Line) parameters are
shown on a scale from "0" to "1", with "1" representing the best
possible performance (no defects) and "0" meaning all sites are bad
(or in chip fabrication terms: all chips on a wafer are outside of
the allowed range of values), on the left side of the chart. If a
tool detracts from yield, the more times the tool is used on a
given workpiece, the lower the yield.
[0033] Referring to FIG. 4, a variance of this method is to analyze
all tools in a side by side comparison. In FIG. 4 the performance
of a set of four tools A, B, C, and D is shown on the same graph
for workpieces which have been processed once, twice or three times
by the same tools A-D. Tools A and C do not appear to detract from
the Mean Yield, while tool D underperforms marginally and tool B
under-performs seriously, which is particularly noticeable after
summarizing the yield of the all workpieces processed third times
by tool B. Manifestly the analysis reveals the fact that action
will be required to deal with the under-performance of tool B.
[0034] Referring to FIG. 5, the method can be also applied with
multi-stage and multi-chamber tools (i.e. tools with more than one
chamber of the kind shown in the schematic diagram in FIG. 6. FIG.
6 shows a tool 90 with multiple stages and multiple tools in each
stage. In the first stage of tool 90 there are three chambers A, B,
and C. In the second stage of tool 90 there are two chambers 1 and
2. Here in a given tool 90 the product passes through two process
stages. In the first stage the product can be processed on any of
the "triplet" chambers identified as A, B, C and in the second
stage can it can be processed on any of the "twin" chambers
identified as 1 and 2. A plot of all chamber-combinations for the
tool can be made on one chart to identify any chamber-combination
which is causing a significant delta in the yield data when the
frequency of usage of the chamber-combinations in question is
increased. In FIG. 5, a plot is shown of the six
chamber-combinations seen in FIG. 6 which include
chamber-combinations ChA-1, ChA-2, ChB-1, ChB-2, ChC-1, and ChC-2.
From the plot it is seen that as the chamber-combination ChB-2 is
used more times the yield is getting lower, and therefore action is
required for this chamber-combination.
[0035] In accordance with the method of this invention the
procedure, assumptions and method include select a group of
operations that share a set of tools (RIE, Plating, Chemical
Polish, Metal Deposition, etc . . . ); acquiring the logistic data
(down to chamber level) and merging with the Health of Line (HOL)
data (e.g. charging HOL) or Yield data.
[0036] If a chamber has a negative effect on HOL/Yield the more the
chamber is used the lower the yield.
[0037] In frequency analysis, using trend HOL_mean vs frequency for
all tool-chambers, the fact that a chamber or tool is bad is
revealed by a monotonic decrease in yield as a function of
frequency of use thereof on a single workpiece. Alternatively,
determine trend HOL percentiles vs frequency for each tool-chamber
and display the curve of Number Of Observations (NOBS) relative to
a second Y axis on the same chart.
[0038] In accordance with this invention, a method is provided for
determining the quality of performance of a tool wherein a tool
includes an individual tool and includes a tool chamber in a
multichamber tool in a manufacturing system. The method comprises
the following steps. Store process data from tools for each one of
a plurality of individual processes for a processed object in a
process database. Store tool performance data for each individual
process for a processed object in a yield database. Develop
statistics for similar tool sets associating data with each of the
similar tool units. Generate yield numbers for each group of the
similar tool units based upon the statistics. Identify well
performing tools and poorly performing tools by using the yield
numbers.
[0039] Preferably generate statistical numbers and associated yield
numbers for each set of shared tools, for all tool combinations
possible, without accounting for tool sequence. Preferably,
identify a poorly performing tool when all combinations with that
tool have a depressed yield, and identify a well performing tool
when all combinations with that tool have an acceptable yield.
Preferably, generate statistical numbers and associated yield
numbers for each tool/chamber frequency. Preferably calculate a
yield number for each frequency. Preferably identify a tool/chamber
as performing at an unacceptable level when yield decreases
monotonically with tool usage. Preferably collect object data from
manufactured objects in a functional test apparatus, passing the
object data to an object testing database, to a data collection
processor, the data collection processor, and a data mining
processor; supplying data from tools to the data collection
processor; supplying data from the data collection processor to a
logistic data base processor; supplying data from the logistic
database to the data mining processor; and employing data mining
processing to determine performance of the tool/chamber. Preferably
employ SQL to summarize data for each object by tool and lot.
Preferably create a frequency table of data by lot, object, and
tool. Preferably create an object yield database. Preferably form a
composite table by joining the frequency table and the object yield
database. Preferably take frequency as an independent variable and
yield as a dependent variable. Preferably determine whether tool
frequency and tool yield correlate.
[0040] In accordance with another aspect of this invention, a
system is provided for identifying performance of tool units in a
manufacturing system performance relative to an acceptable level
including a process database for storing process data from tools
for each one of a plurality of individual processes for a processed
object; a yield database for storing tool unit performance data for
each individual process for a processed object; a data mining
processor for developing statistics for similar tool units
associating data with each of the similar tool units, a yield
analyzer for generating yield numbers for each group of the similar
tool units based upon the statistics, and a performance analyzer
which identifies performance levels of tool units by using the
yield numbers.
[0041] In accordance with still another aspect of this invention a
system is provided for identifying the performance level of at
least one chamber and/or at least one stage in a manufacturing
system with respect to an acceptable level. The system includes a
process database for storing process data from tools for each one
of a plurality of individual processes for a processed object; a
yield database for storing tool performance data for each
individual process for a processed object; a data mining processor
for developing statistics for similar tool units associating data
with each of the similar tool units, a yield analyzer which
generates yield numbers for each group of the similar tool units
based upon the statistics, and a performance analyzer which
identifies well and poorly performing tool units by using the yield
numbers.
[0042] Preferably the data mining processor generates statistical
numbers and associated yield numbers for each set of shared tools,
for all tool combinations possible, without accounting for tool
sequence. Preferably the data mining processor identifies a poorly
performing tool when all combinations with that tool have a
depressed yield. Preferably the data mining processor identifies a
well performing tool when all combinations with that tool have an
acceptable yield. Preferably the data mining processor generates
statistical numbers and associated yield numbers for each
tool/chamber frequency. Preferably the data mining processor
calculates a yield number for each frequency and identifies a
tool/chamber as performing at an unacceptable level when yield
decreases monotonically with tool usage.
[0043] The invention and objects and features thereof will be more
readily apparent from the following detailed description and
appended claims when taken with the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0044] The foregoing and other aspects and advantages of this
invention are explained and described below with reference to the
accompanying drawings, in which:
[0045] FIG. 1A shows a diagram illustrating a series of processing
through a set of Front End of Line (FEOL) process steps into a set
of Back End of Line (BEOL) process steps. Below the FEOL and BEOL
blocks in FIG. 1A is a chart showing the metallization process
cycles from a first metal layer cycle through to the last three
metallization cycles.
[0046] FIG. 1B is a schematic diagram which shows the multiple
cycle metallization processing for workpieces which are being
manufactured and tested by functional test apparatus with data
being collected in a data collecting processor.
[0047] FIG. 1C is a schematic diagram which shows the duplication
of use of the tools stages during the several cycles of the very
large number of metallization process cycles for workpieces which
are being manufactured and tested.
[0048] FIG. 2 is a chart showing a prior art type analysis of the
yield of a process step the after repetition of a process on a set
on two tools with one tool having superior performance.
[0049] FIG. 3A is a chart showing, the trend of the mean yield data
and other statistical parameters for each of several tool
combinations
[0050] FIG. 3B is a chart showing the results of an analysis of the
yield for each tool considered separately by counting the number of
times the tool was used during the process of manufacture of a
given product.
[0051] FIG. 4 is a chart showing the performance of a set of four
tools on the same graph for workpieces which have been processed
once, twice or three times by the same tools.
[0052] FIG. 5 is a chart showing the performance of many
chambers-combinations within a multi-chamber tool
[0053] FIG. 6 is a schematic diagram of a multi-stage,
multi-chamber tool.
[0054] FIG. 7A is a schematic block diagram of a system with
several computers for performing data mining of a manufacturing
process connected via cables for receiving the data from the tools
on a factory floor which houses several tools for manufacturing of
objects and via other cables for receiving the data from functional
test apparatus which tests the objects which were produced by the
tools.
[0055] FIG. 7B shows a modification of the system of FIG. 7A in
which a single central processor computer system is connected via
cables for receiving the data from the tools on the factory floor
and via other cables for receiving the data from the functional
test apparatus for processing in like manner to the functions
provided by the four computers systems shown in FIG. 7A.
[0056] FIG. 8 is a flow chart of a method of processing data
employing the apparatus of FIGS. 7A and 7B.
[0057] FIG. 9A is another flow chart showing a method of processing
data employing the apparatus of both FIG. 7A and FIG. 7B.
[0058] FIG. 9B is a flow chart which is a modification of FIG. 9A
showing a method of processing data employing the apparatus of both
FIG. 7A and FIG. 7B.
[0059] FIG. 10 is a flow chart is provided to account for the
complete processing history of a set of tools shared in a
manufacturing process.
[0060] FIGS. 11A and 11B are flow charts illustrating a method for
identify a bad tool using the tool combination statistics and
analysis, (or tool-chamber combination in case of multi-chamber
tools) and yield statistics.
[0061] FIG. 12 is a flow chart starting with a step of storing
process data from tools (i.e. tool information for each individual
process) for each one of a plurality of individual processes for a
processed object in a tool/chamber information data base.
[0062] FIG. 13 is a chart showing the results of an analysis of the
yield for a single tool considered separately by counting the
number of times the tool was used during the process of manufacture
of a given product.
[0063] FIG. 14 shows the definitions of yield categories employed
in making a yield determination for tools which will fall into the
categories for any good yield, average yield, chip perfect yield,
or chip perfect yield no incomplete chips.
[0064] FIG. 15 is a chart showing the performance of a
multi-chamber tool on the same graph for workpieces which have been
processed once, twice or three times by the same chamber of that
multi-chamber tool.
DESCRIPTION OF THE PREFERRED EMBODIMENT
[0065] FIG. 7A is a schematic block diagram of a system 10 for
performing data mining of a manufacturing process involving a
factory floor 12 housing a plurality of tools for manufacturing of
lots, each of which comprise several workpieces W. The system 10
performs data collection from a series of workpieces W being
manufactured on a factory floor 12 which includes a set of tools
12-1 to 12-4, a Data Collection Processor (DCP) 15, a functional
test apparatus 17, logistic database 16, wafer testing database 20,
and data mining processor 26. The factory floor 12 includes several
stages comprising deposit metal stage 12-1, dielectric deposition
stage 12-2, patterning stage 12-3, and RIE stage 12-4. As described
above in connection with FIG. 1B, each stage includes similar tools
which can perform the same processing step or steps. The
manufacturing process requires recycling of the work through tools
in the process from cycle to cycle on a random basis depending upon
which of the tools at a particular stage is available when a
workpiece W arrives at the stage. During the repetitive cycles, a
large number of intermediate steps must be performed by many other
tools (not shown for convenience of illustration).
[0066] The sets of tool stages 12-1 to 12-4, which include a
deposit metal stage 12-1, a deposit dielectric stage 12-2, a
patterning stage 12-3 and an RIE stage 12-4. Each of those stages
12-1 to 12-4 may include two or more similar tools which can
process a given workpiece W. The two or more similar tools at a
single stage are provided so that when one or more tools at a stage
is/are otherwise occupied with processing or in need of repair
another available tool at that stage can process the workpiece W
without delay. The workpieces W enter the deposit metal stage 12-1
on conveyor line 11A. After processing at deposit metal stage 12-1,
workpiece W moves on conveyor line 11B to dielectric deposition
stage 12-2. After processing at stage 12-2, workpiece W moves on
conveyor line 11C to patterning stage 12-3. After processing at
stage 12-3, workpiece W moves on conveyor line 17A to RIE stage
12-4, which may include where three RIE tools A, B and C in a
single stage as in FIG. 1B.
[0067] The identification of workpieces W processed by individual
tools is supplied on lines 13-1 to 13-4 to the DCP 15. In
particular, each of the tools in stage 12-1 is connected to send
workpiece identification data on line 13-1 to the DCP 15. Each of
the tools in stage 12-2 is connected to send workpiece
identification data on line 13-2 to the DCP 15. Each of the tools
in stage 12-3 is connected to send workpiece identification data on
line 13-3 to the DCP 15. In stage 12-4 the RIE tools are connected
to send workpiece identification data from line 13-4 to cable 13 to
the DCP 15. There may also be some test data collected which is
supplied to the DCP 15, but there is no overall test data supplied
on lines 13-1 to 13-4 as to the effects of processing by each
individual tool upon the overall quality of the workpiece W. There
are other tests made by parametric testers at various stages in the
process of manufacture such as yield data, as distinguished from
functional test data provided by the functional test apparatus 17
at the end of processing.
[0068] After completion of the first cycle of processing by the
four stages 12-1 to 12-4, the workpiece W is recycled along line
14A-14Y to the input line 11 to stage 12-1 and is processed there
by whichever tool is available in stage 12-1 and the sequential
process is repeated at stages 12-2 to 12-4 as described above. The
workpiece W is recycled many times through stages 12-1 to 12-4
repeatedly for manufacture of the metal layers until all of the
metal layers including the metal layer N+1 have been manufactured
in accordance with FIG. 1A. Then the workpiece W leaves the factory
floor 12 on conveyor line 14Z which delivers it for testing to the
functional test apparatus 17 which performs tests after all
processing has been completed, as will be well understood by those
skilled in the art. The data collected by the functional test
apparatus is sent to the DCP 15.
[0069] As each lot and each workpiece in the lot are processed by a
tool, that tool will send data including the Wafer_id, the Lot_id,
and the Tool_id via cable 13-1, 13-2, 13-3 or 13-4 from stage 12-1,
12-2 12-3 or 12-4 respectively and cable 13 to the Data Collection
Processor (DCP) computer system 15 which comprises a general
purpose computer with a computer program which, among other things,
stores the tool processing data for each lot and each workpiece W
therein. In summary, the data sent to the DCP computer system 15 on
cables 13 comprises the identity of each lot (e.g. semiconductor
wafer workpieces W) processed by each of the tools at each of the
stages on the factory floor 12 and each workpiece W and the
processing step(s) performed by tools at each of the stages on the
factory floor 12.
[0070] At the output from the factory floor 12, the workpieces W
are transported along conveyor line 14Z to the conventional
functional test apparatus 17 where the workpieces W are tested for
defects or parameters. The output data from the functional test
apparatus 17 is supplied on cable 18 to the DCP computer system 15
which comprises a general purpose computer with a computer program
which also stores the functional test data for each workpiece W in
each lot of workpieces which will include the Wafer_Id; and the
Lot_id.
[0071] In accordance with current manufacturing technique, there
may be several tools or chambers in a multi-chamber tool in stages
12-1 to 12-4 which provide data relating to processed lots and
workpieces W.
[0072] There is a logistic database computer system 16 which
contains the following data: Lot_id, Wafer_Id, Tool_Id, and Process
Definition ID (PD_ID) which are supplied thereto by cable 22 from
the DCP computer system 15. The logistic database computer system
16 also contains the Process Definition ID (PD_ID) data which is
entered into the DCP computer system. The DCP computer system gets
data from tool log files. The logistic data base computer system 16
supplies the following data: LOT ID, WAFER ID; TOOL ID and the
Process Definition ID (PD_ID) data on cable 23 to the Data Mining
Processor 26.
[0073] Output on cable from the functional test apparatus 17 is
supplied via cable 21 to the Wafer Testing Data Base computer 20
which calculates the yield data for each workpiece and each lot.
The yield data sent to the Wafer Testing Data Base computer 20
comprises results of the testing of workpieces W by the functional
test apparatus 17. The Wafer Testing Database computer 20 supplies
yield data on cable 25 to Data Mining Processor 26.
[0074] FIG. 7B shows a modification of the system 10 of FIG. 7A in
which a single central processor computer system 30 is connected
via cables 13, 13-1, 13-2, 13-3 and 13-4 for receiving the data
from the tools on the factory floor 12 and via cable 18 for
receiving the data from the functional test apparatus 17 for
processing in like manner to the functions provided by the four
computers systems 15, 16, 20 and 26 in FIG. 7A.
[0075] FIG. 8 is a flow chart of a method of processing data
employing the apparatus shown in FIGS. 7A and 7B. Reference will be
made principally to FIG. 7A for clarity of explanation. In step AA,
data from the tools A, B, and C is transmitted via cables 13, 13-1,
13-2, and 13-3 and via the DCP computer system 15 and cable 22. As
a result, data for a processed object such as a workpiece W is
stored in the logistic database computer system 16 in FIG. 7A. Then
in step AB in FIG. 2, the logistic database computer system 16 uses
SQL to summarize data by Lot_id & Tool_id counting the number
of times each object was processed by a given tool.
TABLE-US-00001 TABLE I SQL DATA with Wafer_id, Lot_id, &
Tool_id Lot_id Wafer_id Tool_id Process_step Lot_1 Wafer_11 Tool_A
PD_ID_1 Lot_1 Wafer_12 Tool_A PD_ID_1 Lot_1 Wafer_13 Tool_A PD_ID_1
Lot_1 Wafer_11 Tool_A PD_ID_2 Lot_1 Wafer_12 Tool_A PD_ID_2 Lot_1
Wafer_13 Tool_A PD_ID_2 Lot_1 Wafer_11 Tool_A PD_ID_3 Lot_1
Wafer_12 Tool_A PD_ID_3 Lot_1 Wafer_13 Tool_A PD_ID_3 Lot_1
Wafer_11 Tool_B PD_ID_4 Lot_1 Wafer_12 Tool_B PD_ID_4 Lot_1
Wafer_13 Tool_B PD_ID_4 Lot_1 Wafer_11 Tool_B PD_ID_5 Lot_1
Wafer_12 Tool_B PD_ID_5 Lot_1 Wafer_13 Tool_B PD_ID_5 Lot_1
Wafer_11 Tool_C PD_ID_6 Lot_1 Wafer_12 Tool_C PD_ID_6 Lot_1
Wafer_13 Tool_C PD_ID_6 Lot_2 Wafer_21 Tool_A PD_ID_1 Lot_2
Wafer_22 Tool_A PD_ID_1 Lot_2 Wafer_23 Tool_A PD_ID_1 Lot_2
Wafer_21 Tool_A PD_ID_2 Lot_2 Wafer_22 Tool_A PD_ID_2 Lot_2
Wafer_23 Tool_A PD_ID_2 Lot_2 Wafer_21 Tool_B PD_ID_4 Lot_2
Wafer_22 Tool_B PD_ID_4 Lot_2 Wafer_23 Tool_B PD_ID_4 Lot_2
Wafer_21 Tool_B PD_ID_5 Lot_2 Wafer_22 Tool_B PD_ID_5 Lot_2
Wafer_23 Tool_B PD_ID_5 Lot_2 Wafer_21 Tool_B PD_ID_6 Lot_2
Wafer_22 Tool_B PD_ID_6 Lot_2 Wafer_23 Tool_B PD_ID_6 Lot_2
Wafer_21 Tool_B PD_ID_7 Lot_2 Wafer_22 Tool_B PD_ID_7 Lot_2
Wafer_23 Tool_B PD_ID_7 Lot_2 Wafer_21 Tool_C PD_ID_8 Lot_2
Wafer_22 Tool_C PD_ID_8 Lot_2 Wafer_23 Tool_C PD_ID_8
[0076] In step AC create a frequency table of data, e.g. Lot_id,
Wafer_id & Tool_id shown in Table II below.
TABLE-US-00002 TABLE II Resulting Frequency of the SQL Data With
Lot_id & Tool_id Tool Frequency (Number of Lot_id Wafer_id
Tool_id times lot processed by the Tool) Lot_1 Wafer_11 Tool_A 3
Lot_1 Wafer_12 Tool_A 3 Lot_1 Wafer_13 Tool_A 3 Lot_1 Wafer_11
Tool_B 2 Lot_1 Wafer_12 Tool_B 2 Lot_1 Wafer_13 Tool_B 2 Lot_1
Wafer_11 Tool_C 1 Lot_1 Wafer_12 Tool_C 1 Lot_1 Wafer_13 Tool_C 1
Lot_2 Wafer_21 Tool_A 2 Lot_2 Wafer_22 Tool_A 2 Lot_2 Wafer_23
Tool_A 2 Lot_2 Wafer_21 Tool_B 4 Lot_2 Wafer_22 Tool_B 4 Lot_2
Wafer_23 Tool_B 4 Lot_2 Wafer_21 Tool_C 1 Lot_2 Wafer_22 Tool_C 1
Lot_2 Wafer_23 Tool_C 1
[0077] In step AD create an object yield database, e.g. Lot_id,
Wafer_id; yield parameters (VAR1, VAR2, . . . ); HOL (Health Of
Line) parameters.
TABLE-US-00003 TABLE III Object Yield Database Lot_id Wafer_id All
Good Yield Lot_1 Wafer_11 30 Lot_1 Wafer_12 40 Lot_1 Wafer_13 45
Lot_1 Wafer_11 30 Lot_1 Wafer_12 40 Lot_1 Wafer_13 45 Lot_1
Wafer_11 30 Lot_1 Wafer_12 40 Lot_1 Wafer_13 45 Lot_2 Wafer_21 50
Lot_2 Wafer_22 55 Lot_2 Wafer_23 56 Lot_2 Wafer_21 50 Lot_2
Wafer_22 55 Lot_2 Wafer_23 56 Lot_2 Wafer_21 50 Lot_2 Wafer_22 55
Lot_2 Wafer_23 56
[0078] In step AE join the frequency table and the object yield
database by PRODUCT_ID into a new table shown by Table IV.
TABLE-US-00004 TABLE IV Tool Frequency (Number of All Good times
lot Lot_id Wafer_id Yield processed by the Tool) Tool_id Lot_1
Wafer_11 30 3 Tool_A Lot_1 Wafer_12 40 3 Tool_A Lot_1 Wafer_13 45 3
Tool_A Lot_1 Wafer_11 30 2 Tool_B Lot_1 Wafer_12 40 2 Tool_B Lot_1
Wafer_13 45 2 Tool_B Lot_1 Wafer_11 30 1 Tool_C Lot_1 Wafer_12 40 1
Tool_C Lot_1 Wafer_13 45 1 Tool_C Lot_2 Wafer_21 50 2 Tool_A Lot_2
Wafer_22 55 2 Tool_A Lot_2 Wafer_23 56 2 Tool_A Lot_2 Wafer_21 50 4
Tool_B Lot_2 Wafer_22 55 4 Tool_B Lot_2 Wafer_23 56 4 Tool_B Lot_2
Wafer_21 50 1 Tool_C Lot_2 Wafer_22 55 1 Tool_C Lot_2 Wafer_23 56 1
Tool_C
[0079] In step AF take "Frequency" as an independent variable &
take "yield" as a dependent variable
[0080] In step AG a test is made which is to determine "Do tool
Frequency and the tool Yield correlate?" IN step AG, a generalized
linear model is used to evaluate the correlation between all good
yield and Tool Frequency in accordance with the equation as
follows:
Y=a+b*X. Where: a=intercept; b=slope of the line; Y=all good Yield,
and X=tool Frequency for one tool.
[0081] We test whether b is significantly different from zero, then
we check to see if b is positive or negative. If b is significantly
different from zero, then there is a correlation between yield and
tool frequency.
[0082] If b equals zero (horizontal line) there is no variation in
yield as a function of frequency so tool does not affect yield.
[0083] In step AH select tool one selects a tool. If b is negative,
this means the tool is a bad tool. If b is positive, this means the
tool is a good tool.
[0084] In step Al display the tool frequency analysis plot shown in
FIG. 13.
[0085] FIG. 13 is a chart showing the results of an analysis of the
yield for a single tool considered separately by counting the
number of times the tool was used during the process of manufacture
of a given product.
[0086] In step AJ engineering action (stop tool.) If b is negative
tool is bad, stop the tool.
[0087] FIG. 9A is a flow chart of a method of processing data
employing the apparatus shown in FIGS. 7A and 7B; and reference
will be made principally to FIG. 7A for clarity of explanation.
[0088] In step BA, data from the tools A, B, C is transmitted via
cables 13, 13A, 13B, and 13C and via cable 18 to the DCP computer
system 15. As a result, data for process data from tools for each
one of a plurality of individual processes for a processed object
is stored in a data base in the DCP 15 in FIG. 7A
[0089] In step BB, the DCP 15 transmits the data from step BA to
the logistic database processor 16 which manipulates the data
collected in step BA to correlate tool combination data by
PRODUCT_ID, i.e. generating a tool combination or path for each
PRODUCT_ID.
[0090] In step BC logistic database processor 16 creates a "Tool
Path" Table for the Lot_id & Wafer_id; and the Tool Path
(Combination) of the object through the various tools on the
factory floor 12. Table V below shows a table of combinations of
tool paths for the factory floor 12 of FIG. 7A during eight runs of
lots 1 and 2 through the three stages 1-3.
TABLE-US-00005 TABLE V TOOL PATH COMBINATIONS STAGE STAGE STAGE
Tool_Com- RUN Lot_id Wafer_id 1 2 3 bination 1 1 1_1 1A 2C 3C A_C 1
1 1_2 1A 2C 3C A_C 1 1 1_3 1A 2C 3C A_C 2 2 2_1 1C 2A 3B A_B_C 2 2
2_2 1C 2A 3B A_B_C 2 2 2_3 1C 2A 3B A_B_C
[0091] In step BD a Product Yield Data Base is created comprising
PRODUCT_ID (Lot_id & Wafer_id), Yield Parameters (VAR1, VAR2, .
. . ), and HOL (Health Of Line) Parameters as shown in Table VI
TABLE-US-00006 TABLE VI PRODUCT YIELD TABLE All Good Chain Limited
Lot_id Wafer_id Yield Yield HOL 1 1_1 50 62 99 1 1_2 51 62 98 1 1_3
52 63 98 2 1_1 41 59 90 2 1_2 42 59 91 2 1_3 43 60 93
[0092] In step BE Join the data from TOOL_PATH in Table V and
Product Yield Database by PRODUCT_ID in Table VI into a new
Composite Table seen in TABLE VII below.
TABLE-US-00007 TABLE VII COMPOSITE TABLE All Chain Good Limited RUN
Lot_id Wafer_id STAGE 1 STAGE 2 STAGE 3 TOOl_Combination Yield
Yield HOL 1 1 1_1 1A 2C 3C A_C 50 62 99 1 1 1_2 1A 2C 3C A_C 51 62
98 1 1 1_3 1A 2C 3C A_C 52 63 98 2 2 2_1 1C 2A 3B A_B_C 41 59 90 2
2 2_2 1C 2A 3B A_B_C 42 59 91 2 2 2_3 1C 2A 3B A_B_C 43 60 93
[0093] In step BF of FIG. 3A, display on the monitor of the data
mining processor 26 the data in TABLE VII of the Yield vs.
TOOL_PATH data for assisting engineering personnel in making
judgments as to which tools are performing badly. The display
emphasizes combinations containing a bad tool so the displays
therefore stand out from the surrounding data being displayed.
[0094] FIG. 9B is a flow chart of a method of processing data
employing the apparatus shown in FIGS. 7A and 7B; which is a
modification of FIG. 9A but which is the same form block BA to
block BE. There is no block BF in FIG. 9B and block BG follows
block BE. Block BG in FIG. 9B is a decision block which reads:
[0095] Using Statistical Procedures is any Path significantly
Different?
[0096] The statistical model is called ANOVA (Analysis Of Variance)
which can be used to identify group difference. If p-value<0.1,
we have found that there is tool combination difference.
[0097] In step BH select tool one selects a tool. Referring to FIG.
9B in step BH, in a given tool the product has two process stages,
in the first stage it can be processed on any of the "triplet"
chambers identified as A, B, C and in the second stage can it can
be processed on any of the "twin" chambers identified as 1 and
2.
[0098] In step BI the Yield versus Frequency data for a tool
determined to be affecting yield is displayed on a computer monitor
for engineering judgment. The analysis has determined that there is
a dependence between Yield and the number of times the tools was
used. Therefore the tool requires human intervention. The tool
needs either calibration, maintenance work or repairs. Depending on
the severity of the problem and the availability of other tool the
engineer has to decide if and when to stop tool, in step BJ.
[0099] In FIG. 10, a flow chart is provided to account for the
complete processing history of the set of tools shared in a
manufacturing process.
[0100] In step CA store process data from tools for each one of a
plurality of individual processes for a processed object in a
process database.
[0101] Processing data, meaning tool information for each
individual process for the processed object, is stored in a
database as indicated above.
[0102] In step CB Yield numbers for each object (product or wafer)
exiting a production line are stored in a yield database, as in
step AD in FIG. 2 and step BD in FIGS. 3A and 3B.
[0103] In step CC statistics are built (as number of times a tool
was used, combinations of tools the processed objects have seen,
combinations of tool-chambers processed objects have seen) for tool
sets (also known as tools that perform same or similar operations,
like plating, RIE, Metal depositions, etc) that are used more than
once. In this step, the system develops shared statistics for all
processes performed on similar tool units with the same
capabilities associating a group of numbers with each of the
similar tool units.
[0104] In step CD Yield numbers are generated for each group of
similar tool units based upon the above statistics.
[0105] In step CE identify the bad tool units by using the above
yield numbers.
[0106] FIGS. 11A and 11B are flow charts illustrating a method for
identify a bad tool using the tool combination statistics and
analysis, (or tool-chamber combination in case of multi-chamber
tools) and yield statistics.
[0107] In step DA of FIG. 11A, store process data from tools for
each one of a plurality of individual processes for a processed
object in a process data base.
[0108] In step DB, store tool information for each individual
process for the processed object in a processing database.
[0109] In step DC, generate statistical numbers and associated
yield numbers for each set of shared tools, for all tool
combinations possible, without accounting for tool sequence.
[0110] In step DD, identify tool as bad when all combinations with
that tool have a depressed yield.
[0111] In FIG. 11B, steps DA-DC are the same but step DD is
replaced by step DE, that identifies a tool as good when all
combinations with that tool have an acceptable and/or increased
yield.
[0112] FIG. 12 is a flow chart starting with step EA of storing
process data from tools (i.e. tool information for each individual
process) for each one of a plurality of individual processes for a
processed object in a tool/chamber information data base.
[0113] In step EB, store tool yield numbers for each exiting
product in a yield data database.
[0114] In step EC, store tool or chamber information for each
individual process for the processed object, in a processing data
database.
[0115] In step ED, generate statistical numbers and associated
yield numbers for each tool/chamber frequency.
[0116] In step EE, for each frequency calculate a yield number for
each frequency.
[0117] In step EF, identify a tool/chamber as bad when yield
decreases monotonically with the tool usage.
[0118] The most common definition of Yield is the ratio of the
number of functional working product divided by the total number of
products produced. (2 good out of 5 produced: yield is or 0.4 or
40%) Fabricators usually employ a couple of other definitions of
Yield as described next.
[0119] Yield can be defined for parameters that have numerical
values and are measured during (or at the end of) processing flow.
For these parameters the fabricator has determined an upper and a
lower permissible value called "SPEC." Certain parameter have only
a one sided specification (short-circuit, open-circuit, etc.)
[0120] Fabricators define Health of Line (HOL) as a simple
multiplication of the Yields for a determined set of
parameters.
[0121] FIG. 14 shows the definitions of yield categories employed
in making a yield determination for tools which will fall into the
categories for any good yield, average yield, chip perfect yield,
or chip perfect yield no incomplete chips.
[0122] FIG. 15 is a chart showing the performance of a
multi-chamber tool on the same graph for workpieces which have been
processed once, twice or three times by the same chamber of a
multi-chamber tool. In this case the tool has four chambers A, B, C
and D and the workpiece can be processed by any one of those
chambers at a given PASS. FIG. 15 illustrates that as more
processing is done by chamber A, the yield is reduced and by
fitting a line through the points of chamber A, there is a negative
slope (b<0), so chamber A needs engineering action such as
stopping, repairs, maintenance, calibration or replacement.
[0123] Another aspect of the method of this invention is that it is
effective not only when a tool is used for the exactly same process
several times in one cycle but can also be used when a tool
performs different processes in a product manufacturing cycle.
[0124] While this invention has been described in terms of the
above specific embodiment(s), those skilled in the art will
recognize that the invention can be practiced with modifications
within the spirit and scope of the appended claims, i.e. that
changes can be made in form and detail, without departing from the
spirit and scope of the invention. Accordingly all such changes
come within the purview of the present invention and the invention
encompasses the subject matter of the following claims.
* * * * *