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`Case 4:20-cv-00991 Document 1-6 Filed 12/31/20 Page 1 of 10 PageID #: 182
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`EXHIBIT F
`
`EXHIBIT F
`
`

`

`(12) United States Patent
`Stirton
`
`US006836691B1
`(10) Patent No.:
`US 6,836,691 B1
`(45) Date of Patent:
`Dec. 28, 2004
`
`(54) METHOD AND APPARATUS FOR
`FILTERING METROLOGY DATA BASED ON
`COLLECTION PURPOSE
`
`6,479,200 B1
`6,529,282 B1
`2002/0135781 A1
`
`11/2002 Stirton ........................ 430/30
`3/2003 Stirton et al. .....
`... 356/630
`9/2002 Singh et al. ................ 356/601
`
`
`
`(75) Inventor: James B. Stirton, Austin, TX (US)
`
`(73) Assignee: Advanced Micro Devices, Inc., Austin,
`TX (US)
`
`(*) Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 0 days.
`
`(21) Appl. No.: 10/427,620
`(22) Filed:
`May 1, 2003
`
`OTHER PUBLICATIONS
`Kenneth W. Tobin, Thomas P KarnoSwski, Fred Lakhani,
`“Technology Consideration For Future Semiconductor Data
`Management Systems', Oak Ridge National Laboratory 1,
`Oak Ridge, TN, USA.*
`Kenneth W. Tobin, Thomas P KarnoSwski, Fred Kakhani,
`“Integrated applications of inspection data in the Semicon
`ductor manufacturing environment', Oak Ridge National
`Laboratory 1, Oak Ridge, TN, USA.*
`(List continued on next page.)
`Primary Examiner Leo Picard
`
`(51) Int. CI.7 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - G06F 19/00
`
`ASSistant Examiner-Carlos R. Ortiz
`
`... 700/108; 702/85
`(52) U.S. Cl. ...............
`(58) Field of Search ........................... 700/108, 95, 110,
`700/117; 702/85; 709/108, 320, 712/228;
`71425,54,438/3. 345/70s
`
`(56)
`
`References Cited
`U.S. PATENT DOCUMENTS
`
`5,864,773 A * 1/1999 Barna et al. .................. 702/85
`5,867,276 A 2/1999 McNeil et al. .............. 356/445
`5,877,860 A 3/1999 Borden ....................... 356/376
`5,880,838 A 3/1999 Marx et al. ................. 356/351
`5,896.294. A * 4/1999 Chow et al. ................ 700/121
`6,051,348 A
`4/2000 Marinaro et al. ............. 430.30
`6,081,334. A
`6/2000 Grimbergen et al. ....... 356/357
`6,141,107 A 10/2000 Nishi et al. ................. 356/401
`6,245,584 B1
`6/2001 Marinaro et al. ............. 438/14
`6,263,255 B1
`7/2001 Tan et al. ................... 700/121
`6,319,884 B2 11/2001 Leduc et al................. 510/175
`6,383,888 B1
`5/2002 Stirton................ 438/401
`6,423.977 B1
`7/2002 Hayasaki et al. ...... 250/559.19
`6,433,878 B1
`8/2002 Niu et al. ................... 356/603
`6,456.899 B1 * 9/2002 Gleason et al. ............. 700/212
`6,473,665 B2 * 10/2002 Mugibayashi et al. ...... 700/110
`
`(74) Attorney, Agent, or Firm Williams, Morgan &
`AmerSon
`ABSTRACT
`(57)
`A method includes collecting metrology data related to the
`processing of workpieces in a plurality of tools. Context data
`for the metrology data is generated. The context data
`includes collection purpose data. The metrology data is
`filtered based on the collection purpose data. A process
`control activity related to one of the tools is conducted based
`on the filtered metrology data. A System includes at least one
`metrology tool, a computer, and a proceSS controller. The
`metrology tool is configured to collect metrology data
`related to the processing of workpieces in a plurality of
`tools. The computer is configured to generate context data
`for the metrology data, the context data including collection
`purpose data. The proceSS controller is configured to filter
`the metrology databased on the collection purpose data and
`conduct a process control activity related to one of the tools
`based on the filtered metrology data.
`
`20 Claims, 2 Drawing Sheets
`
`Case 4:20-cv-00991 Document 1-6 Filed 12/31/20 Page 2 of 10 PageID #: 183
`
`Collect metrology data related to the
`200- processing of worses in a plurality of
`3S
`
`El
`
`Generate context data for the metrology
`210
`- data, the context data including collection
`purpose data
`
`220
`
`Filter the metrology databased on the
`collection purpose data
`
`Conduct aE. control activity related
`230- to one of
`e tools based on the filtered
`metrology data
`
`

`

`US 6,836,691 B1
`Page 2
`
`OTHER PUBLICATIONS
`R. Sherman, E. Tirosh, Z. Smilansky, “Automatic Defect
`Classification System for Semiconductor Wafers”, Machine
`Vision Applications in Industrial Inspection, SPIE vol. 1907,
`p. 72, May 1993.*
`T.P. Karnowski, K.W. Tobin, R.K. Ferrell, and F. Lakhani,
`“Content Based Image Retrievel for Semiconductor Manu
`facturing”, IS&T/SPIE’s 12th International Symposium on
`Electronic Imaging: Science and Technology, San Jose Con
`vention Center, Jan. 2000.*
`Kenneth W. Tobin, Thomas PKarnowski, Fred Lakhani, Jul.
`2000, “Technology Consideration For Future Semiconduc
`tor Data Management Systems', Oak Ridge National Labo
`ratory 1, Oak Ridge, TN, USA.*
`Kenneth W. Tobin, Thomas PKarnowski, Fred Lakhani, Jan.
`2001, “Integrated applications of inspection data in the
`Semiconductor manufacturing environment', Oak Ridge
`National Laboratory 1, Oak Ridge, TN, USA.*
`
`U.S. Appl. No. 09/827,453, entitled “Method of Controlling
`Stepper Process Parameters Based Upon Optical Properties
`of Incoming Process Layers, and System for Accomplishing
`Same,” filed Apr. 6, 2001.
`U.S. Appl. No. 10/005,486, entitled “Method of Using
`Scatterometry Measurements to Control Stepper Process
`Parameters,” filed Nov. 8, 2001.
`U.S. Appl. No. 10/084,987, entitled “Method of Using High
`Yielding Spectra Scatterometry Measurements to Control
`Semiconductor Manufacturing Processes, and Systems for
`Accomplishing Same,” filed Feb. 28, 2002.
`U.S. Appl. No. 10/404,026, entitled “Method of Using
`Adaptive Sampling Techniques to Quantify Tool Perfor
`mance, and System for Performing Same,” filed Apr. 3,
`2003.
`
`* cited by examiner
`
`Case 4:20-cv-00991 Document 1-6 Filed 12/31/20 Page 3 of 10 PageID #: 184
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`

`

`U.S. Patent
`
`Dec. 28, 2004
`
`Sheet 1 of 2
`
`US 6,836,691 B1
`
`10
`
`90
`
`1 OO
`
`Process
`Controller
`120
`
`Tool
`
`Tool
`
`TOO
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`30
`4-1
`
`Tool
`40A
`
`Tool
`4OB
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`Tool
`40C
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`40
`Tool /
`40D
`
`Awar
`C3SC
`
`Erio ||
`
`TOO
`OO
`
`TOO
`OO
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`
`
`TOO
`60A
`
`TOO
`6OB
`up
`
`TOO
`OO
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`60
`4-1
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`50
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`-
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`Process
`Controller
`120
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`70
`4-1
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`80
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`
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`Process
`Controller
`120
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`Fault
`Monitor
`130
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`Case 4:20-cv-00991 Document 1-6 Filed 12/31/20 Page 4 of 10 PageID #: 185
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`TOO
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`TOO
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`TOO
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`TOOl
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`Too
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`TOO
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`Figure 1
`
`

`

`U.S. Patent
`
`Dec. 28, 2004
`
`Sheet 2 of 2
`
`US 6,836,691 B1
`
`
`
`Collect metrology data related to the
`200- processing of workpieces in a plurality of
`tools
`
`21 O
`
`Generate context data for the metrology
`data, the context data including collection
`purpose data
`
`220
`
`Filter the metrology data based on the
`Collection purpose data
`
`Case 4:20-cv-00991 Document 1-6 Filed 12/31/20 Page 5 of 10 PageID #: 186
`
`230
`
`Conduct a process control activity related
`to one of the tools based on the filtered
`metrology data
`
`Figure 2
`
`

`

`US 6,836,691 B1
`
`1
`METHOD AND APPARATUS FOR
`FILTERING METROLOGY DATA BASED ON
`COLLECTION PURPOSE
`
`BACKGROUND OF THE INVENTION
`1. Field of the Invention
`This invention relates generally to an industrial process,
`and, more particularly, to a method and apparatus for filter
`ing metrology data based on collection purpose in a Semi
`conductor device manufacturing environment
`2. Descripiion of the Related Art
`There is a constant drive within the Semiconductor indus
`try to increase the quality, reliability and throughput of
`integrated circuit devices, e.g., microprocessors, memory
`devices, and the like. This drive is fueled by consumer
`demands for higher quality computers and electronic devices
`that operate more reliably. These demands have resulted in
`a continual improvement in the manufacture of Semicon
`ductor devices, e.g., transistors, as well as in the manufac
`ture of integrated circuit devices incorporating Such transis
`tors. Additionally, reducing the defects in the manufacture of
`the components of a typical transistor also lowers the overall
`cost per transistor as well as the cost of integrated circuit
`devices incorporating Such transistors.
`Generally, a set of processing StepS is performed on a
`wafer using a variety of processing tools, including photo
`lithography Steppers, etch tools, deposition tools, polishing
`tools, rapid thermal processing tools, implantation tools, etc.
`One technique for improving the operation of a Semicon
`ductor processing line includes using a factory wide control
`System to automatically control the operation of the various
`processing tools. The manufacturing tools communicate
`with a manufacturing framework or a network of processing
`modules. Each manufacturing tool is generally connected to
`an equipment interface. The equipment interface is con
`nected to a machine interface which facilitates communica
`tions between the manufacturing tool and the manufacturing
`framework. The machine interface can generally be part of
`an advanced process control (APC) system. The APC system
`initiates a control Script based upon a manufacturing model,
`which can be a Software program that automatically
`retrieves the data needed to execute a manufacturing pro
`CCSS.
`Often, Semiconductor devices are Staged through multiple
`manufacturing tools for multiple processes, generating data
`relating to the quality of the processed Semiconductor
`devices. Pre-processing and/or post-processing metrology
`data is collected on a regular basis, generally in accordance
`with a Sampling plan, for process control purposes. The
`collected metrology data is used by the process controllers
`for the tools. Operating recipe parameters are calculated by
`the proceSS controllers based on the performance model and
`the metrology information to attempt to achieve post
`processing results as close to a proceSS target value as
`possible. Reducing variation in this manner leads to
`increased throughput, reduced cost, higher device
`performance, etc., an of which equate to increased profit
`ability.
`Metrology data is also used for other purposes not related
`to process control. One Such use is for fault detection and
`classification (FDC). Fault monitors apply FDC techniques
`to identify devices or tools with fault conditions. For
`example, if a particular device has a critical dimension
`outside a predetermined range, it is flagged as being defec
`tive. The wafer may be reworked, the die may be marked
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`2
`defective, or the wafer may be Scrapped, depending on the
`magnitude and nature of the fault condition. Process tools
`may be monitored during their processing runs. If an
`anomaly is observed during the processing, the tool may be
`shut down for maintenance. The wafers processed by the
`tool may be flagged for Subsequent metrology to determine
`if the tool anomaly caused a degradation of the devices
`formed thereon. Again, the Suspect waferS may be reworked
`or Scrapped.
`Typically, when a process controller gathers metrology
`data to update its control model or generate a control action
`for Subsequent processing, it retrieves metrology data
`related to wafers processed in the tool or tools under its
`control and employs that data to perform its control task. The
`data retrieved includes metrology data collected through the
`regular Sampling plans implemented in the facility, and the
`metrology data collected for other purposes. Some of the
`metrology data does not accurately reflect the State of the
`process or the devices manufactured. For example, devices
`processed by a tool that was malfunctioning may have
`characteristics that were affected by the malfunction (Le., a
`Special cause) rather than by normal process variation (i.e.,
`common cause). Employing this data for use in process
`control routines may introduce a Source of variation that
`cannot be addressed by the process controller and thus
`reduce the effectiveness of the process controller.
`The present invention is directed to overcoming, or at
`least reducing the effects of, one or more of the problems Set
`forth above.
`
`SUMMARY OF THE INVENTION
`One aspect of the present invention is Seen in a method for
`filtering metrology data. The method includes collecting
`metrology data related to the processing of workpieces in a
`plurality of tools. Context data for the metrology data is
`generated. The context data includes collection purpose
`data. The metrology data is filtered based on the collection
`purpose data. A process control activity related to one of the
`tools is conducted based on the filtered metrology data.
`Another aspect of the present invention is seen in a System
`including at least one metrology tool, a computer, and a
`process controller. The metrology tool is configured to
`collect metrology data related to the processing of work
`pieces in a plurality of tools. The computer is configured to
`generate context data for the metrology data, the context
`data including collection purpose data. The process control
`ler is configured to filter the metrology data based on the
`collection purpose data and conduct a process control activ
`ity related to one of the tools based on the filtered metrology
`data.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`The invention may be understood by reference to the
`following description taken in conjunction with the accom
`panying drawings, in which like reference numerals identify
`like elements, and in which:
`FIG. 1 is a simplified block diagram of a manufacturing
`System in accordance with one embodiment of the present
`invention; and
`FIG. 2 is a simplified flow diagram of a method for
`filtering metrology data in accordance with another embodi
`ment of the present invention.
`While the invention is susceptible to various modifica
`tions and alternative forms, specific embodiments thereof
`have been shown by way of example in the drawings and are
`
`

`

`US 6,836,691 B1
`
`3
`herein described in detail. It should be understood, however,
`that the description herein of Specific embodiments is not
`intended to limit the invention to the particular forms
`disclosed, but on the contrary, the intention is to cover all
`modifications, equivalents, and alternatives falling within
`the Spirit and Scope of the invention as defined by the
`appended claims.
`
`DETAILED DESCRIPTION OF SPECIFIC
`EMBODIMENTS
`
`Illustrative embodiments of the invention are described
`below. In the interest of clarity, not all features of an actual
`implementation are described in this specification. It will of
`course be appreciated that in the development of any Such
`actual embodiment, numerous implementation-specific
`decisions must be made to achieve the developerS Specific
`goals, Such as compliance with System-related and busineSS
`related constraints, which will vary from one implementa
`tion to another. Moreover, it will be appreciated that Such a
`development effort might be complex and time-consuming,
`but would nevertheless be a routine undertaking for those of
`ordinary skill in the art having the benefit of this disclosure.
`Referring to FIG. 1, a simplified block diagram of an
`illustrative manufacturing system 10 is provided. In the
`illustrated embodiment, the manufacturing System 10 is
`adapted to fabricate Semiconductor devices. Although the
`invention is described as it may be implemented in a
`Semiconductor fabrication facility, the invention is not So
`limited and may be applied to other manufacturing environ
`ments. The techniques described herein may be applied to a
`Variety of Workpieces or manufactured items, including, but
`not limited to, microprocessors, memory devices, digital
`Signal processors, application specific integrated circuits
`(ASICs), or other devices. The techniques may also be
`applied to workpieces or manufactured items other than
`Semiconductor devices.
`A network 20 interconnects various components of the
`manufacturing System 10, allowing them to exchange infor
`mation. The illustrative manufacturing System 10 includes a
`plurality of tools 30–80. Each of the tools 30-80 may be
`coupled to a computer (not shown) for interfacing with the
`network 20. The tools 30-80 are grouped into sets of like
`tools, as denoted by lettered Suffixes. For example, the Set of
`tools 30A-30C represent tools of a certain type, such as a
`chemical mechanical planarization tool. A particular wafer
`or lot of wafers progresses through the tools 30-80 as it is
`being manufactured, with each tool 30-80 performing a
`Specific function in the process flow. Exemplary processing
`tools for a Semiconductor device fabrication environment
`include photolithography Steppers, etch tools, deposition
`tools, polishing tools, rapid thermal processing tools,
`implantation tools, etc. Exemplary metrology tools include
`thickneSS metrology tools, Scanning electron microscopes,
`optical metrology tools, electrical measurement tools, etc.
`The tools 30-80 are illustrated in a rank and file grouping for
`illustrative purposes only. In an actual implementation, the
`tools 30-80 may be arranged in any physical order or
`grouping. Additionally, the connections between the tools in
`a particular grouping are meant to represent connections to
`the network 20, rather than interconnections between the
`tools 30-80.
`Portions of the invention and corresponding detailed
`description are presented in terms of Software, or algorithms
`and Symbolic representations of operations on data bits
`within a computer memory. These descriptions and repre
`sentations are the ones by which those of ordinary skill in the
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`art effectively convey the substance of their work to others
`of ordinary skill in the art. An algorithm, as the term is used
`here, and as it is used generally, is conceived to be a
`Self-consistent Sequence of Steps leading to a desired result.
`The Steps are those requiring physical manipulations of
`physical quantities. Usually, though not necessarily, these
`quantities take the form of optical, electrical, or magnetic
`Signals capable of being Stored, transferred, combined,
`compared, and otherwise manipulated. It has proven con
`Venient at times, principally for reasons of common usage,
`to refer to these signals as bits, values, elements, Symbols,
`characters, terms, numbers, or the like.
`It should be borne in mind, however, that all of these and
`Similar terms are to be associated with the appropriate
`physical quantities and are merely convenient labels applied
`to these quantities. Unless Specifically Stated otherwise, or as
`is apparent from the discussion, terms Such as “processing
`or “computing” or “calculating” or “determining” or “dis
`playing or the like, refer to the action and processes of a
`computer System, or Similar electronic computing device,
`that manipulates and transforms data represented as
`physical, electronic quantities within the computer System's
`registers and memories into other data Similarly represented
`as physical quantities within the computer System memories
`or registers or other Such information Storage, transmission
`or display devices.
`An exemplary information eXchange and process control
`framework suitable for use in the manufacturing system 10
`is an Advanced Process Control (APC) framework, such as
`may be implemented using the Catalyst System offered by
`KLA-Tencor, Inc. The Catalyst system uses Semiconductor
`Equipment and Materials International (SEMI) Computer
`Integrated Manufacturing (CIM) Framework compliant sys
`tem technologies and is based the Advanced Process Control
`(APC) Framework. CIM (SEMI E81-0699-Provisional
`Specification for CIM Framework Domain Architecture)
`and APC (SEMI E93-0999-Provisional Specification for
`CIM Framework Advanced Process Control Component)
`specifications are publicly available from SEMI, which is
`headquartered in Mountain View, Calif.
`A manufacturing execution system (MES) server 90
`directs the high level operation of the manufacturing System
`10. The MES server 90 monitors the status of the various
`entities in the manufacturing System 10 (i.e., lots, tools
`30–80) and controls the flow of articles of manufacture (e.g.,
`lots of Semiconductor wafers) through the process flow. A
`database server 100 may be provided for storing data related
`to the Status of the various entities and articles of manufac
`ture in the process flow. The database server 100 may store
`information in one or more data stores 110. The data may
`include pre-process and post-process metrology data, tool
`States, lot priorities, etc.
`The processing and data Storage functions are distributed
`amongst the different computers or workstations in FIG. 1 to
`provide general independence and central information Stor
`age. Of course, different numbers of computers and different
`arrangements may be used without departing from the Spirit
`and Scope of the instant invention.
`Process controllers 120 may be associated with one or
`more of the process tools 30-80. The process controllers 120
`determine control actions for controlling Selected ones of the
`tools 30-80 serving as process tools based on metrology
`data collected during the fabrication of wafers (i.e., by others
`of the tools 30-80 serving as metrology tools). The particu
`lar control models used by the process controllers 120
`depend on the type of process tool 30-80 being controlled,
`
`

`

`6
`particular region of wafer (e.g., the periphery region), the
`fault monitor 130 may request that additional metrology data
`be collected for that particular region.
`The MES server 90 may receive requests from various
`consumers to collect metrology data. These consumerS may
`be fault detection entities or process control entities, for
`example. The metrology tool (e.g., one of the tool 30-80)
`collects the metrology data and the data is Stored in the data
`store 110. The metrology data may be stored directly by the
`metrology tool 30-80 or the data may be returned to the
`MES server 90 for storage. The metrology data is stored also
`with associated context data that includes identification data
`and collection purpose data.
`Exemplary identification data includes lot identification
`number (ID), wafer ID, location data (e.g., location of
`measurement on die or wafer), process-operation data (e.g.,
`last completed Step in the fabrication process), etc. The
`collection purpose data indicates the initial purpose for the
`collection of the metrology data. For example, the purpose
`may be process control Sampling, fault detection Sampling,
`targeted fault detection, etc.
`In one illustrative embodiment of the present invention,
`the collection purpose data is used to filter the metrology
`data for Subsequent uses. For example, a process controller
`120 would conventionally employ all metrology data for a
`particular tool 30-80 and process-operation operation for
`updating the States of its control model and generating a
`control action for modifying an operating recipe parameter
`for the tool 30-80. By using the collection purpose data to
`filter the metrology data, metrology data collected for fault
`detection purposes, where the likelihood of a fault being
`present is higher, can be excluded. Filtering the metrology
`data in this manner may improve the performance of the
`process controller 120 by removing outlier data that exhibits
`variation from a Source other than normal proceSS Variation.
`If the process controller 120 were to act on metrology data
`that included special causes of variation (e.g., tool faults), it
`would attempt to shift the process in a direction that might
`actually increase variation and reduce the Stability of the
`proceSS.
`In Some cases, metrology data collected for process
`control purposes may also be used in fault detection. The
`MES server 90 would initially indicate that the collection
`purpose would be process control Sampling. However, if the
`metrology data was later used in a fault detection analysis
`and the wafer was determined to be faulty, the MES server
`90 or fault monitor 130 may change the collection purpose
`such that the metrology data would be filtered out for
`Subsequent process control activities. For example, the MES
`server 90 may set the collection purpose data to a value
`indicating a known faulty wafer. However, if the metrology
`data indicated a fault condition that could be tracked back to
`a process variation cause, the metrology data may still be
`useful for process control purposes and the MES Server may
`leave the collection purpose data unchanged.
`Some fault detection Sampling data may also be randomly
`collected. In Such cases, a defect condition is not Suspected,
`and the data is used for process oversight. Hence, it may be
`possible that the data collected for fault detection purposes
`may still be useful for process control. In these cases, where
`the initial purpose was fault detection, but no defect
`identified, the MES server 90 or fault monitor 130 may
`change the collection purpose data to a value indicating this
`condition, Such that the metrology data So collected could
`Still be used for process control purposes.
`Table 1 below list exemplary collection purpose codes
`that may be stored with the collected metrology data. The list
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`and the particular metrology data collected for use in con
`junction with the control models depends on the feature
`being formed by the particular process tool 30-80. The
`control models may be developed empirically using com
`monly known linear or non-linear techniques. The control
`models may be relatively simple equation-based models
`(e.g., linear, exponential, Weighted average, etc.) or a more
`complex model, Such as a neural network model, principal
`component analysis (PCA) model, partial least Squares
`projection to latent structures (PLS) model. The specific
`implementation of the control models may vary depending
`on the modeling techniques Selected and the process being
`controlled. The Selection and development of the particular
`control models would be within the ability of one of ordinary
`skill in the art, and accordingly, the control models are not
`described in greater detail herein for clarity and to avoid
`obscuring the instant invention.
`An exemplary process control Scenario involves the con
`trol of a gate electrode critical dimension (CD) in a transistor
`Structure. Various processes and process variables may be
`controlled to affect the gate electrode CD. For example, a
`photoresist mask is used to pattern the gate electrode. The
`photolithography processes used to form the mask may
`affect the dimensions of the pattern and thus the dimensions
`of the gate electrode formed by an etch proceSS using the
`mask. Exposure time and energy may be controlled to affect
`the dimensions of the mask. The parameters (e.g., etch time,
`plasma power, etch gas makeup and concentration, etc.) of
`the etch process may also affect the CD of the completed
`gate electrode and may be controlled by a process controller
`120. The processes and variables described above that affect
`the gate electrode CD are not exhaustive. Other processes
`may be performed that have an impact of the CD and other
`variables of those processes may be controlled.
`In some embodiments, a fault monitor 130 executing on
`a workstation 135 may be provided for monitoring fault
`conditions with the tools 30-80 and/or devices manufac
`tured. For example, a particular tool 30-80 may be perform
`ing poorly or feature formed on a device may have a
`dimension outside an acceptable range of values. The fault
`monitor 130 may implement one or more fault detection and
`classification (FDC) models to evaluate the condition of the
`various entities or devices. Metrology data is employed by
`the fault monitor 130 to identify fault conditions with
`various tools 30-80 or workpieces and also to update the
`FDC model(s) employed to identify the degraded condi
`tions. The fault monitor 130 may use the metrology data
`collected for process control purposes to perform its defect
`analysis. For example, metrology data collected during a
`photolithography process for forming the gate electrode etch
`mask may be used to control the photolithography tool
`30–80. The fault monitor 130 may also review the data to
`determine if the dimensions of the mask are within accept
`able limits. If the mask dimensions are outside the accept
`able range, the photoresist layer may be removed and the
`wafer reworked to form a new photoresist layer.
`In other cases, the fault monitor 130 may target certain
`tools 30-80 or wafers for fault analysis and issue its own
`metrology requests for data related to the targeted tool 30-80
`or wafer. For example, if a particular tool parameter is
`outside a range of expected values during a processing run,
`waferS processed during that run may be targeted for metrol
`ogy to determine if the parameter excursion introduced a
`defect in the processed device. The fault monitor 130 may
`also initiate metrology events in cases where the probability
`of defect is higher than a baseline probability. For example,
`if a process is known to produce a higher defect rate on a
`
`

`

`7
`is intended to be illustrative and not exhaustive or limiting
`to the application of the present invention.
`
`US 6,836,691 B1
`
`TABLE 1.
`
`Collection Purpose Codes
`
`CP Code
`
`Collection Purpose
`
`O1
`O2
`O3
`88
`99
`
`Process Control Sampling
`Fault Detection Sampling
`Targeted Fault Detection
`Fault Detection - no fault identified
`Known Defective
`
`The following examples illustrate the use of the collection
`purpose codes for filtering the metrology data. ProceSS
`control data is collected in accordance with a Sampling plan
`implemented by the MES server 90 or other sampling
`controller (not shown). The collection purpose code for this
`data is set at “01.” The fault monitor 130 requests metrology
`data for random FDC oversight. The collection purpose code
`for this data is set at “02.” The fault monitor 130 may also
`use the “01” data for FDC oversight. In some cases the fault
`monitor 130 may identify that a particular tool parameter
`was outside expected limits during a processing run or that
`the health of a particular tool 30-80 has degraded to a level
`indicating a need for maintenance or troubleshooting. The
`fault monitor 130 may request additional metrology data be
`collected for wafers processed during the particular proceSS
`run or by the degraded tool 30-80. This targeted fault
`detection data would have a collection purpose code of
`“03'analysis of the “01” and “02” indicates that a fault
`condition may exist, additional metrology data may be
`requested. Such additional metrology data would have a
`collection purpose code of “03.”
`If the fault monitor 130 identifies a faulty die or wafer, it
`may change the collection purpose code of the “01” or “02”
`or “O3’ data to “99. If no fault is identified for the “O2 or
`“03’ data, the fault monitor 130 may change the collection
`purpose code to "88. In Some embodiments, user interven
`tion may be required before the collection purpose code can
`be changed. For example, the metrology data collected for
`targeted fault detection may indicate that the processed
`devices are Satisfactory, however, variation may have been
`introduced by the observed condition that led to the target
`ing. In Such cases, the metrology data may not be useful for
`proceSS control due to the presence of the additional Source
`of variation, and the collection purpose code may remain
`unchanged. User input may be used to determine if the
`variation is of this type, and the collection purpose code
`should not be changed.
`When the process controller 120 gathers metrology data
`for process control purposes (e.g., State update or control
`action generation), it filters the metrology data, So that data
`that is leSS useful for process control purposes is ignored.
`For example, the process controller 120 may gather “01.”
`“02,” and “88” data and exclude the metrology data where
`fault conditions are more likely to exist (Le., the “03” and
`“99” data). Filtering the data in this manner may increase the
`efficacy of the proceSS controller 120 because non-proceSS
`Sources of variation; may be removed from the data Set used
`for the proceSS control purpose.
`Turning