`Stirton
`
`I 1111111111111111 11111 111111111111111 IIIII IIIII IIIII IIIII IIIIII IIII 11111111
`US006836691Bl
`
`(10) Patent No.:
`(45) Date of Patent:
`
`US 6,836,691 Bl
`Dec. 28, 2004
`
`(54) METHOD AND APPARATUS FOR
`FILTERING METROLOGY DATA BASED ON
`COLLECTION PURPOSE
`
`6,479,200 Bl
`6,529,282 Bl
`2002/0135781 Al
`
`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 O days.
`
`(21) Appl. No.: 10/427,620
`
`(22)
`
`Filed:
`
`May 1, 2003
`
`(51)
`(52)
`(58)
`
`(56)
`
`Int. Cl.7 . ... ... .. ... ... ... ... .. ... ... ... ... ... .. ... ... .. G06F 19/00
`U.S. Cl. .......................................... 700/108; 702/85
`Field of Search ........................... 700/108, 95, 110,
`700/117; 702/85; 709/108, 320; 712/228;
`714/25, 54; 438/12; 345/708
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`5,864,773 A *
`5,867,276 A
`5,877,860 A
`5,880,838 A
`5,896,294 A *
`6,051,348 A
`6,081,334 A
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`6,245,584 Bl
`6,263,255 Bl *
`6,319,884 B2
`6,383,888 Bl
`6,423,977 Bl
`6,433,878 Bl
`6,456,899 Bl *
`6,473,665 B2 *
`
`Barna et al. . . . . . . . . . . . . . . . . . . 702/85
`McNeil et al. .............. 356/445
`Borden ....................... 356/376
`Marx et al. ................. 356/351
`Chow et al. ................ 700/121
`Marinaro et al.
`............. 430/30
`Grimbergen et al.
`....... 356/357
`Nishi et al. ................. 356/401
`Marinaro et al.
`............. 438/14
`Tan et al.
`................... 700/121
`Leduc et al. ................ 510/175
`Stirton ....................... 438/401
`Hayasaki et al.
`...... 250/559.19
`Niu et al.
`................... 356/603
`Gleason et al. ............. 700/212
`Mugibayashi et al. ...... 700/110
`
`1/1999
`2/1999
`3/1999
`3/1999
`4/1999
`4/2000
`6/2000
`10/2000
`6/2001
`7/2001
`11/2001
`5/2002
`7/2002
`8/2002
`9/2002
`10/2002
`
`OTHER PUBLICATIONS
`
`Kenneth W. Tobin, Thomas P Karnoswski, Fred Lakhani,
`"Technology Consideration For Future Semiconductor Data
`Management Systems", Oak Ridge National Laboratoryl,
`Oak Ridge, TN, USA*
`Kenneth W. Tobin, Thomas P Karnoswski, Fred Kakhani,
`"Integrated applications of inspection data in the semicon(cid:173)
`ductor manufacturing environment", Oak Ridge National
`Laboratoryl, 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
`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 data based 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
`
`Collect metrology data related to the
`
`- processing of workpieces in a plurality of
`200
`
`tools
`
`data, the context data including collection
`purpose data
`
`l
`210 - Generate context data for the metrology
`l
`220 - Filter the metrology data based on the
`l
`230 - ~~~dnu:~f l.':f:1~ ba~1~d1 ~~1ih~Y filt~~!'t
`
`collection purpose data
`
`metrology data
`
`Applied Materials, Inc. Ex. 1001
`Applied v. Ocean, IPR Patent No. 6,836,691
`Page 1 of 9
`
`
`
`US 6,836,691 Bl
`Page 2
`
`OIBER 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 Retrieve! for Semiconductor Manu(cid:173)
`facturing", IS&T/SPIE's 12th International Symposium on
`Electronic Imaging: Science and Technology, San Jose Con(cid:173)
`vention Center, Jan. 2000. *
`Kenneth W. Tobin, Thomas P Karnowski, Fred Lakhani, Jul.
`2000, "Technology Consideration For Future Semiconduc(cid:173)
`tor Data Management Systems", Oak Ridge National Labo(cid:173)
`ratoryl, 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 Laboratoryl, 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(cid:173)
`mance, and System for Performing Same," filed Apr. 3,
`2003.
`
`* cited by examiner
`
`Applied Materials, Inc. Ex. 1001
`Applied v. Ocean, IPR Patent No. 6,836,691
`Page 2 of 9
`
`
`
`U.S. Patent
`
`Dec. 28, 2004
`
`Sheet 1 of 2
`
`US 6,836,691 Bl
`
`10
`\..
`
`90
`
`100
`
`Process
`Controller
`120
`
`30
`
`Tool
`30A
`
`Tool
`30B
`
`Tool
`30C
`
`/
`
`Tool
`40A
`
`Tool
`40B
`
`Tool
`40C
`
`40
`Tool ~
`40D
`
`Tool
`50A
`
`Tool
`508
`
`Tool
`50C
`
`50
`
`/
`
`20
`
`135
`
`Tool
`60A
`
`Tool
`608
`
`60
`.-/
`
`Process
`Controller
`120
`
`Fault
`Monitor
`130
`
`Tool
`70A
`
`Tool
`708
`
`Tool
`70C
`
`Tool
`BOA
`
`Tool
`808
`
`Tool
`BOC
`
`70
`
`80
`
`/
`
`/
`
`Figure 1
`
`Process
`'------1 Controller
`120
`
`Applied Materials, Inc. Ex. 1001
`Applied v. Ocean, IPR Patent No. 6,836,691
`Page 3 of 9
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`U.S. Patent
`
`Dec. 28, 2004
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`Sheet 2 of 2
`
`US 6,836,691 Bl
`
`Collect metrology data related to the
`200__.. processing of workpieces in a plurality of
`tools
`
`Generate context data for the metrology
`21 a_ data, the context data including collection
`purpose data
`
`. .,
`
`220_
`
`Filter the metrology data based on the
`collection purpose data
`
`230 _
`
`Conduct a process control activity related
`to one of the tools based on the filtered
`metrology data
`
`Figure 2
`
`Applied Materials, Inc. Ex. 1001
`Applied v. Ocean, IPR Patent No. 6,836,691
`Page 4 of 9
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`
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`US 6,836,691 Bl
`
`1
`METHOD AND APPARATUS FOR
`FILTERING METROLOGY DATA BASED ON
`COLLECTION PURPOSE
`
`BACKGROUND OF THE INVENTION
`
`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
`5 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
`15 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
`20 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
`25 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.
`
`1. Field of the Invention
`This invention relates generally to an industrial process,
`and, more particularly, to a method and apparatus for filter(cid:173)
`ing metrology data based on collection purpose in a semi- 10
`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(cid:173)
`ductor devices, e.g., transistors, as well as in the manufac(cid:173)
`ture of integrated circuit devices incorporating such transis(cid:173)
`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(cid:173)
`lithography steppers, etch tools, deposition tools, polishing
`tools, rapid thermal processing tools, implantation tools, etc. 30
`One technique for improving the operation of a semicon(cid:173)
`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 35
`modules. Each manufacturing tool is generally connected to
`an equipment interface. The equipment interface is con(cid:173)
`nected to a machine interface which facilitates communica(cid:173)
`tions between the manufacturing tool and the manufacturing
`framework. The machine interface can generally be part of 40
`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(cid:173)
`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- 55
`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(cid:173)
`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 65
`outside a predetermined range, it is flagged as being defec(cid:173)
`tive. The wafer may be reworked, the die may be marked
`
`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-
`45 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(cid:173)
`ler is configured to filter the metrology data based on the
`collection purpose data and conduct a process control activ-
`50 ity related to one of the tools based on the filtered metro logy
`data.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`The invention may be understood by reference to the
`following description taken in conjunction with the accom(cid:173)
`panying drawings, in which like reference numerals identify
`like elements, and in which:
`FIG. 1 is a simplified block diagram of a manufacturing
`60 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(cid:173)
`ment of the present invention.
`While the invention is susceptible to various modifica(cid:173)
`tions and alternative forms, specific embodiments thereof
`have been shown by way of example in the drawings and are
`
`Applied Materials, Inc. Ex. 1001
`Applied v. Ocean, IPR Patent No. 6,836,691
`Page 5 of 9
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`US 6,836,691 Bl
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`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 5
`the spirit and scope of the invention as defined by the
`appended claims.
`
`4
`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-
`10 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
`15 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(cid:173)
`playing" or the like, refer to the action and processes of a
`20 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
`25 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 ESl-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
`40 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
`45 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-
`50 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
`55 amongst the different computers or workstations in FIG. 1 to
`provide general independence and central information stor(cid:173)
`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
`65 of the tools 30-80 serving as metrology tools). The particu(cid:173)
`lar control models used by the process controllers 120
`depend on the type of process tool 30-80 being controlled,
`
`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(cid:173)
`related constraints, which will vary from one implementa(cid:173)
`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- 30
`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 35
`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(cid:173)
`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 60
`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(cid:173)
`sentations are the ones by which those of ordinary skill in the
`
`Applied Materials, Inc. Ex. 1001
`Applied v. Ocean, IPR Patent No. 6,836,691
`Page 6 of 9
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`5
`and the particular metrology data collected for use in con(cid:173)
`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(cid:173)
`monly known linear or non-linear techniques. The control 5
`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 10
`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 15
`described in greater detail herein for clarity and to avoid
`obscuring the instant invention.
`An exemplary process control scenario involves the con(cid:173)
`trol of a gate electrode critical dimension (CD) in a transistor
`structure. Various processes and process variables may be 20
`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 25
`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 30
`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 35
`a workstation 135 may be provided for monitoring fault
`conditions with the tools 30-80 and/or devices manufac(cid:173)
`tured. For example, a particular tool 30-80 may be perform(cid:173)
`ing poorly or feature formed on a device may have a
`dimension outside an acceptable range of values. The fault 40
`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 45
`FDC model(s) employed to identify the degraded condi(cid:173)
`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 50
`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(cid:173)
`able limits. If the mask dimensions are outside the accept(cid:173)
`able range, the photoresist layer may be removed and the 55
`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 60
`outside a range of expected values during a processing run,
`wafers processed during that run may be targeted for metrol(cid:173)
`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 65
`of defect is higher than a baseline probability. For example,
`if a process is known to produce a higher defect rate on a
`
`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
`
`Applied Materials, Inc. Ex. 1001
`Applied v. Ocean, IPR Patent No. 6,836,691
`Page 7 of 9
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`
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`US 6,836,691 Bl
`
`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
`02
`03
`88
`99
`
`Process Control Sampling
`Fault Detection Sampling
`Targeted Fault Detection
`Fault Detection -
`no fault identified
`Known Defective
`
`20
`
`25
`
`8
`context data for the metrology data is generated. The context
`data includes collection purpose data. In block 220, the
`metrology data is filtered based on the collection purpose
`data. In block 230, a process control activity related to one
`5 of the tools is conducted based on the filtered metrology
`data.
`The particular embodiments disclosed above are illustra(cid:173)
`tive only, as the invention may be modified and practiced in
`different but equivalent manners apparent to those skilled in
`10 the art having the benefit of the teachings herein.
`Furthermore, no limitations are intended to the details of
`construction or design herein shown, other than as described
`in the claims below. It is therefore evident that the particular
`embodiments disclosed above may be altered or modified
`15 and all such variations are considered within the scope and
`spirit of the invention. Accordingly, the protection sought
`herein is as set forth in the claims below.
`What is claimed:
`1. A method, comprising:
`collecting metrology data related to the processing of
`workpieces in a plurality of tools;
`generating context data for the metrology data, the con(cid:173)
`text data including collection purpose data;
`filtering the metrology data based on the collection pur(cid:173)
`pose data; and
`conducting a process control activity related to one of the
`tools based on the filtered metrology data.
`2. The method of claim 1, wherein generating the context
`data further comprises generating identification data asso(cid:173)
`ciated with the metrology data, and filtering the metrology
`data further comprises filtering the metrology data based on
`the identification data and the collection purpose data.
`3. The method of claim 1, wherein generating the context
`35 data further comprises generating collection purpose data
`indicating at least one of a process control sampling purpose,
`a fault detection sampling purpose, and a targeted fault
`detection purpose.
`4. The method of claim 1, further comprising:
`identifying a fault condition for a workpiece based on the
`metrology data; and
`changing the collection purpose data responsive to iden(cid:173)
`tifying the fault condition.
`5. The method of claim 1, further comprising:
`identifying an absence of a fault condition for a workpiece
`based on the metrology data; and
`changing the collection purpose data responsi