`Stirton
`
`(10) Patent N0.:
`(45) Date of Patent:
`
`US 6,836,691 B1
`Dec. 28, 2004
`
`US006836691B1
`
`(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
`
`(51) Int. Cl.7 .............................................. .. G06F 19/00
`(52) US. Cl. ............. ..
`700/108; 702/85
`(58) Field of Search ......................... .. 700/108, 95, 110,
`700/117; 702/85; 709/108, 320; 712/228;
`714/25, 54; 438/12; 345/708
`
`(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 a1. ..
`356/445
`5,877,860 A
`3/1999 Borden ..................... .. 356/376
`5,880,838 A
`3/1999 Marx et a1. ............... .. 356/351
`
`5,896,294 A * 4/1999 Chow et a1. . . . . . . . .
`. . . .. 700/121
`6,051,348 A
`4/2000 Marinaro et a1. ........... .. 430/30
`6,081,334 A
`6/2000 Grimbergen et a1. ..... .. 356/357
`6,141,107 A 10/2000 Nishi et a1. ............... .. 356/401
`6,245,584 B1
`6/2001 Marinaro et a1. ........... .. 438/14
`6,263,255 B1 * 7/2001 Tan et a1. .............. .. 700/121
`6,319,884 B2 11/2001 Leduc et a1. .
`510/175
`6,383,888 B1
`5/2002 Stirton .................. .. 438/401
`6,423,977 B1
`7/2002 Hayasaki et a1. .... .. 250/559.19
`6,433,878 B1
`8/2002 Niu et a1. ................. .. 356/603
`6,456,899 B1 * 9/2002 Gleason et a1. ........... .. 700/212
`6,473,665 B2 * 10/2002 Mugibayashi et a1. .... .. 700/110
`
`OTHER PUBLICATIONS
`
`Kenneth W. Tobin, Thomas P KarnosWski, Fred Lakhani,
`“Technology Consideration For Future Semiconductor Data
`Management Systems”, Oak Ridge National Laboratory1,
`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
`Laboratory1, Oak Ridge, TN, USA.*
`
`(List continued on next page.)
`
`Primary Examiner—Leo Picard
`Assistant Examiner—Carlos R. OrtiZ
`(74) Attorney, Agent, or Firm—Williams, Morgan &
`Amerson
`
`(57)
`
`ABSTRACT
`
`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
`?ltered based on the collection purpose data. A process
`control activity related to one of the tools is conducted based
`on the ?ltered metrology data. Asystem includes at least one
`metrology tool, a computer, and a process controller. The
`metrology tool is con?gured to collect metrology data
`related to the processing of Workpieces in a plurality of
`tools. The computer is con?gured to generate context data
`for the metrology data, the context data including collection
`purpose data. The process controller is con?gured to ?lter
`the metrology data based on the collection purpose data and
`conduct a process control activity related to one of the tools
`based on the ?ltered metrology data.
`
`20 Claims, 2 Drawing Sheets
`
`Collect metrology data related to the
`200‘ processing of worlépieices in a plurality of
`on s
`
`Generate oontext data for the metrology
`210
`‘.1 data, the context data including colleotlon
`purpose data
`
`220
`
`Filter the metrology data based on the
`collection purpose data
`
`Conduct atgrooess control activity related
`2304., to one of
`a tools based on the ?ltered
`metrology data
`
`PDF Solutions v Ocean Semiconductor, IPR2022-01196
`PDF Exhibit 1001, Page 1 of 9
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`
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`US 6,836,691 B1
`Page 2
`
`OTHER PUBLICATIONS
`
`R. Sherman, E. Tirosh, Z. Smilansky, “Automatic Defect
`Classi?cation 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 P KarnoWski, Fred Lakhani, Jul.
`2000, “Technology Consideration For Future Semiconduc
`tor Data Management Systems”, Oak Ridge National Labo
`ratory1, Oak Ridge, TN, USA.*
`Kenneth W. Tobin, Thomas P KarnoWski, Fred Lakhani, Jan.
`2001, “Integrated applications of inspection data in the
`semiconductor manufacturing environment”, Oak Ridge
`National Laboratory1, Oak Ridge, TN, USA.*
`
`US. 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,” ?led Apr. 6, 2001.
`US. Appl. No. 10/005,486, entitled “Method of Using
`Scatterometry Measurements to Control Stepper Process
`Parameters,” ?led Nov. 8, 2001.
`US. Appl. No. 10/084,987, entitled “Method of Using High
`Yielding Spectra Scatterometry Measurements to Control
`Semiconductor Manufacturing Processes, and Systems for
`Accomplishing Same,” ?led Feb. 28, 2002.
`US. Appl. No. 10/404,026, entitled “Method of Using
`Adaptive Sampling Techniques to Quantify Tool Perfor
`mance, and System for Performing Same,” ?led Apr. 3,
`2003.
`
`* cited by examiner
`
`PDF Solutions v Ocean Semiconductor, IPR2022-01196
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`
`
`U.S. Patent
`
`Dec. 28,2004
`
`Sheet 1 of2
`
`US 6,836,691 B1
`
`1O
`\
`
`A
`
`Process
`Controller
`Q
`
`90
`
`30
`T |
`T |
`T l
`00
`00
`O0
`a 3_0A _’ 30B " 30o ‘/
`
`40
`Tool _> Tool _) Tool _) Tool /
`M 595
`40C
`49D
`
`50
`To |
`T I
`T l
`59A % 1J5 _’ 50o ‘/
`
`/
`20
`
`P
`60
`Tool / rocess
`Tool
`‘’ 60A _’ 60B
`COr11t2r8ller
`
`135
`
`Fault
`Monitor
`w
`
`00
`Q0
`O0
`a M a m “’ 70c ‘/
`
`80
`TQO|
`Tool
`Tool
`» M a 525 ‘T 800 ‘/
`
`V
`
`Figure 1
`
`Process
`Controller
`L22
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`PDF Solutions v Ocean Semiconductor, IPR2022-01196
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`
`
`U.S. Patent
`
`Dec. 28,2004
`
`Sheet 2 of2
`
`US 6,836,691 B1
`
`Collect metrology data related to the
`200% processing of workpieces in a plurality of
`tools
`
`Generate context data for the metrology '
`210% data, the context data including collection
`purpose data
`
`229
`‘J
`
`Filter the metrology data based on the
`collection purpose data
`
`7
`
`Conduct a process control activity related
`230
`_/ to one of the tools based on the filtered
`metrology data
`
`Figure 2
`
`PDF Solutions v Ocean Semiconductor, IPR2022-01196
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`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 ?lter
`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
`cess.
`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 pro?t
`ability.
`Metrology data is also used for other purposes not related
`to process control. One such use is for fault detection and
`classi?cation (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 ?agged as being defec
`tive. The Wafer may be reworked, the die may be marked
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`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 ?agged 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 re?ect 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
`?ltering 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 ?ltered based on the collection
`purpose data. Aprocess control activity related to one of the
`tools is conducted based on the ?ltered 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 con?gured to
`collect metrology data related to the processing of Work
`pieces in a plurality of tools. The computer is con?gured to
`generate context data for the metrology data, the context
`data including collection purpose data. The process control
`ler is con?gured to ?lter 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 ?ltered 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 simpli?ed block diagram of a manufacturing
`system in accordance With one embodiment of the present
`invention; and
`FIG. 2 is a simpli?ed ?oW diagram of a method for
`?ltering metrology data in accordance With another embodi
`ment of the present invention.
`While the invention is susceptible to various modi?ca
`tions and alternative forms, speci?c embodiments thereof
`have been shoWn by Way of example in the draWings and are
`
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`3
`herein described in detail. It should be understood, hoWever,
`that the description herein of speci?c embodiments is not
`intended to limit the invention to the particular forms
`disclosed, but on the contrary, the intention is to cover all
`modi?cations, equivalents, and alternatives falling Within
`the spirit and scope of the invention as de?ned 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 speci?cation. It Will of
`course be appreciated that in the development of any such
`actual embodiment, numerous implementation-speci?c
`decisions must be made to achieve the developers’ speci?c
`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 bene?t of this disclosure.
`Referring to FIG. 1, a simpli?ed 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 speci?c 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
`speci?c function in the process ?oW. 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 ?le 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 speci?cally 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
`Speci?cation for CIM FrameWork Domain Architecture)
`and APC (SEMI E93-0999-Provisional Speci?cation for
`CIM FrameWork Advanced Process Control Component)
`speci?cations 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 How of articles of manufacture (e. g.,
`lots of semiconductor Wafers) through the process ?oW. 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 ?oW. 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,
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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 speci?c
`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
`classi?cation (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
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`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 identi?cation data
`and collection purpose data.
`Exemplary identi?cation data includes lot identi?cation
`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 ?lter 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
`?lter 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 ?ltered 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
`identi?ed, 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
`
`PDF Solutions v Ocean Semiconductor, IPR2022-01196
`PDF Exhibit 1001, Page 7 of 9
`
`
`
`US 6,836,691 B1
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`7
`is intended to be illustrative and not exhaustive or limiting
`to the application of the present invention.
`
`TABLE 1
`
`Collection Purpose Codes
`
`CP Code
`
`Collection Purpose
`
`01
`O2
`O3
`88
`99
`
`Process Control Sampling
`Fault Detection Sampling
`Targeted Fault Detection
`Fault Detection — no fault identi?ed
`Known Defective
`
`The following examples illustrate the use of the collection
`purpose codes for ?ltering 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 identi?es a faulty die or Wafer, it
`may change the collection purpose code of the “01” or “02”
`or “03” data to “99.” If no fault is identi?ed for the “02” 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 ?lters 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
`ef?cacy 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 noW to FIG. 2, a simpli?ed flow diagram of a
`method for ?ltering metrology data in accordance With
`another embodiment of the present invention is provided. In
`block 200, metrology data related to the processing of
`Workpieces in a plurality of tools is