throbber
Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 1 of 19 PageID #: 192
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 1 of 19 PageID #: 192
`
`EXHIBIT G
`
`EXHIBIT G
`
`

`

`(12) United States Patent
`Purdy
`
`(10) Patent No.:
`(45) Date of Patent:
`
`US 8,676,538 B2
`Mar. 18, 2014
`
`USOO8676538B2
`
`(54) ADJUSTING WEIGHTING OFA
`PARAMETER RELATING TO FAULT
`DETECTION BASED ON ADETECTED
`FAULT
`
`(75) Inventor: Matthew A. Purdy, Austin, TX (US)
`(73) Assignee: Advanced Micro Devices, Inc.,
`Sunnyvale, CA (US)
`
`(*) Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 1564 days.
`
`(21) Appl. No.: 10/979,309
`
`(22) Filed:
`
`Nov. 2, 2004
`
`(65)
`
`Prior Publication Data
`US 2006/OO95232A1
`May 4, 2006
`
`(51) Int. Cl.
`G06F II/30
`G06F 7/40
`G06F 9/00
`B23O 1700
`(52) U.S. Cl.
`USPC ................ 702/183; 73/865.9; 438/5; 438/14:
`700/96; 700/110; 700/121; 702/182: 702/185:
`702/187; 702/189
`
`(2006.01)
`(2006.01)
`(2011.01)
`(2006.01)
`
`(58) Field of Classification Search
`USPC ........... 702/185, 1,33, 34, 35, 36, 40, 57,58,
`702/59, 81, 82, 83, 84, 108, 113, 114, 115,
`702/117, 118, 127, 179, 181, 182, 183,187,
`702/189: 73/865.8, 865.9; 438/5, 6, 7, 8, 9,
`438/10, 11, 12, 13, 14, 15, 16, 17, 18;
`700/1, 11, 21, 79,90, 95, 96, 108, 109,
`700/110, 117, 118, 119, 120, 121, 159, 174,
`700/175; 708/100, 105, 200; 714/1, 25, 37,
`714/48, 100
`
`IPC ..................... B23B 49/00; B23Q 15/00,15/007,
`B23Q 15/12, 17/00, 17/904, 17/952, 17/10,
`B23Q 17/12, 17/20: G05B 13/00; G06F 11/00,
`G06F 11/30, 11/3058, 11/32, 17/00, 17/40,
`GO6F 19/OO
`See application file for complete search history.
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`2,883,255 A * 4, 1959 Anderson ....................... 346/34
`2,897,638 A * 8/1959 Maker ........
`451.5
`3,461,547 A * 8/1969 Di Curcio ......................... 438.7
`(Continued)
`
`FOREIGN PATENT DOCUMENTS
`
`T 2003
`WOO3,058699 A1
`WO
`1, 2004
`WO WO 2004/003822 A1
`WO WO 2004/105101 A2 12/2004
`
`OTHER PUBLICATIONS
`
`Cinar, A. etal. “Statistical Process and Controller Performance Moni
`toring. A Tutorial on current methods and future directions' Ameri
`can Control Conference, vol. 4, Jun. 2, 1999: pp. 2625-2639,
`XPO 10344696.
`
`(Continued)
`Primary Examiner — Edward Cosimano
`(57)
`ABSTRACT
`A method, apparatus and a system, for provided for perform
`ing a dynamic weighting technique for performing fault
`detection. The method comprises processing a workpiece and
`performing a fault detection analysis relating to the process
`ing of the workpiece. The method further comprises deter
`mining a relationship of a parameter relating to the fault
`detection analysis to a detected fault and adjusting a weight
`ing associated with the parameter based upon the relationship
`of the parameter to the detected fault.
`31 Claims, 8 Drawing Sheets
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 2 of 19 PageID #: 193
`
`so N.
`
`80
`
`
`
`
`
`
`
`Analyze Fault Data to determine if it is a
`significant fault
`
`Receive External Input
`regarding causes or non-causes of faults)
`
`S.:
`
`Determine whether to add, subtract, or not change
`factors relating to faults
`
`a
`
`
`
`ES
`
`Dynamically reduce weight to
`factor(s) that caused fault
`
`870
`
`

`

`US 8,676.538 B2
`Page 2
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`5,070,468 A * 12/1991 Ninomi et al. ............... 7O2,185
`5,287.284 A * 2/1994 Sugino et al. ..
`7OO/97
`5,658.423 A * 8/1997 Angell et al. ..................... 438.9
`5,711,849 A
`1/1998 Flamm et al. .............. 156,643.1
`5,786,023 A
`7, 1998 Maxwell et al. .................. 427.8
`5,825,482 A 10/1998 Nikoonahad et al.
`356/237
`6,119,074 A * 9/2000 Sarangapani ......
`702,185
`6,232,134 B1
`5, 2001 Farber et al.
`438.9
`6,238,937 B1 *
`5/2001 Toprac et al.
`438.9
`6,368,883 B1 * 4/2002 Bode et al. ..
`... 438/14
`6.405,096 B1* 6/2002 Toprac et al. ..
`700,121
`6,442.496 B1
`8/2002 Pasadyn et al. ................. TO2/83
`6,521,080 B2 *
`2/2003 Balasubramhanya
`et al. ........................ 156,345.24
`6,549,864 B1 * 4/2003 Potyrailo ........................ TO2/81
`6,564,114 B1* 5/2003 Toprac et al.
`700,121
`6,582,618 B1* 6/2003 Toprac et al. ................... 216.59
`6,590,179 B2* 7/2003 Tanaka et al. ..
`219,121.43
`6,616,759 B2 * 9/2003 Tanaka et al. ..
`... 118.63
`6,675,137 B1 *
`1/2004 Toprac et al. ..................... 703/2
`6,678,569 B2 *
`1/2004 Bunkofskeet al.
`700,108
`6,706,543 B2 * 3/2004 Tanaka et al. ......
`... 438/14
`6,740,534 B1
`5/2004 Adams, III et al.
`... 438/14
`6,789,052 B1* 9/2004 Toprac .............................. 703/2
`6,834,213 B1* 12/2004 Sonderman et al.
`700,121
`6.853,920 B2 *
`2/2005 Hsiung et al. ..................... 7O2/1
`6,859,739 B2 * 2/2005 Wegerich et al.
`TO2/32
`6,865,509 B1* 3/2005 Hsiung et al. ..
`702, 182
`6,871,114 B1* 3/2005 Green et al. .................. TOOf 110
`6,912,433 B1* 6/2005 Chong et al. .................. TOOf 110
`7,024,335 B1 * 4/2006 Parlos .........
`702, 182
`7,043,403 B1* 5/2006 Wang et al. .
`702,185
`7,054,786 B2* 5/2006 Sakano et al.
`702183
`78.33 38. Sin
`aw sy-
`ouilmi et al.
`7,151,976 B2 12/2006 Lin ............................... TOOf 108
`7.212,952 B2 * 52007 Watanabeet al.
`702/183
`7,328,126 B2 * 2/2008 Chamness ..................... TO2, 182
`2002/0062162 A1* 5, 2002 Bunkofskeet al. ........... TOOf 108
`2002/0072882 A1* 6/2002 Kruger et al. ..................... 703/2
`2002/0107858 A1* 8, 2002 Lundahl et al. ............... 7O7/1OO
`
`1/2003 Bell et al. ...................... 438,689
`2003/0008507 A1
`2003/0055523 A1* 3, 2003 Bunkofskeet all
`TOOf 108
`2003/0065462 A1* 4/2003 Potyrailo ...
`702/81
`2003/007.4603 A1* 4/2003 Bungert et al. ................. 714/37
`2003/0109951 A1* 6/2003 Hsiung et al. ................. TOOf 108
`2003/0136511 A1* 7/2003 Balasubramhanya
`et al. ........................ 156,345.25
`2003/0144746 A1* 7/2003 Hsiung et al. ................... TOO/28
`2004/0002776 A1
`1/2004 Bickford ......................... TOO.30
`2004/004.0001 A1* 2, 2004 Miller et al. .
`... 716.f4
`2004/0101983 A1* 5/2004 Jones et al. ..
`438/14
`2004/0259276 A1* 12, 2004 Yue et al. .....
`... 438.5
`2005, OO60103 A1* 3, 2005 Chamness
`702/30
`2005, 0071034 A1* 3, 2005 Mitrovic
`TOOf 121
`2005/0071035 A1* 3/2005 Strang.
`... 700,121
`2005, 0071036 A1* 3, 2005 Mitrovic
`... 700,121
`2005/0071037 A1* 3/2005 Strang ........................... TOOf 121
`2005/0071038 A1* 3/2005 Strang ........................... TOOf 121
`2005, 0071039 A1* 3, 2005 Mitrovic .........
`... 700,121
`2005, 0141782 A1* 6/2005 Guralnik et al. .............. 382.276
`2005/0146709 A1
`7/2005 Oh et al.
`TO2,189
`2005. O149297 A1* 7, 2005 Guralnik et al.
`... 702/182
`2005/0159922 A1* 7/2005 Hsiung et al. ...
`... 700,121
`2005, 0171627 A1* 8, 2005 Funk et al. ...
`... 700,121
`2005. O187649 A1* 8, 2005 Funk et al. ......
`... 701 114
`2005/0203696 A1* 9, 2005 Watanabe et al.
`TOOf 108
`2005/02161 14 A1* 9/2005 Hsiung et al. ...
`... 438/14
`2005/0221514 A1* 10/2005 Pasadyn et al. .
`T14f746
`2005/0268.197 A1* 12/2005 Wold ...........
`2006/0025879 A1* 2/2006 Purdy ...................... TOOf 101
`2006/0074590 A1* 4/2006 Bailey et al. .................. TO2, 182
`2006/011 1804 A1* 5/2006 Lin ............................... TOOf 110
`OTHER PUBLICATIONS
`Yue, H.H. et al.: “Weighted Principal Component Analysis and its
`Applications to Improve FDC Performance” Decision and Control.
`2004. CDC. 43 IEEE Conference on Nassau, Bahamas Dec. 14-17,
`2004; vol. 4, pp. 4262-4267, XPO 10794.793.
`& 8
`H. Yue and R. Lam; “Monitoring Etch Tool Health Using Weighted
`PCA'; AEC/APC Symposium XV, Sematech, Sep. 13-18, 2003;
`Colorado Springs, CO; XP009060799.
`PCT International Search Report; Feb. 9, 2006.
`
`
`
`* cited by examiner
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 3 of 19 PageID #: 194
`
`

`

`U.S. Patent
`
`Mar. 18, 2014
`
`Sheet 1 of 8
`
`US 8,676,538 B2
`
`
`
`3
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 4 of 19 PageID #: 195
`
`lf
`O
`y
`
`C
`l
`wa
`
`

`

`U.S. Patent
`
`Mar. 18, 2014
`
`Sheet 2 of 8
`
`US 8,676,538 B2
`
`
`
`
`
`SJØJeAA SS33OJAI
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 5 of 19 PageID #: 196
`
`

`

`U.S. Patent
`
`Mar. 18, 2014
`
`Sheet 3 of 8
`
`US 8,676,538 B2
`
`
`
`
`
`
`
`
`
`
`
`
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 6 of 19 PageID #: 197
`
`

`

`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 7 of 19 PageID #: 198
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 7 of 19 PageID #: 198
`
`U.S. Patent
`
`Mar. 18, 2014
`
`Sheet 4 of 8
`
`US 8,676,538 B2
`
`Sfiw839:—
`
`Sam—
`
`:EEoUat:
`
`3Sam388m
`
`Q3de$8on
`
`5Sam£88m
`
`3END38on
`
`
`
`8a..Bouéaw
`
`«:5
`
`Sun3am5am
`
`5:530Ev:EEoUEM.5530camE
`
`«Sagan—oakmagi—E:9:535
`
`
`
`
` A:xEVN«its:«Om
`
`
`
`38an5Baaéécmuseumafl:Efifii€233;323:2
`
`
`
`
`
`
`
`
`
`658.5E88Bomémo85809:“?ANVQEESE@353on555flawm
`
`
`
`
`
`can
`
`
`
`BEBoméfiu2390983.AQEMEESI935$onsumac—2*”Him
`A9A0EaBo
`
`
`
`vEDUFA
`
`
`
`
`
`3E88BocéwO05:23thQGbEESE@353on8.5Boy—
`
`
`
`howflgSH:n—ufl
`
`
`
`
`
`
`

`

`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 8 of 19 PageID #: 199
`8e
`aPO
`7.
`199m
`g
`
`A8#umD6,|7m6,P89S1U
`
`%mm5505
`
`_tmm0t2a4Pw.aScU.
`
`OaDM
`
`1Mmm
`
`aml0m51aew«MEmcow
`
`,m8&3qu3mm825m38m30H
`
`SmcomW32:853mm32m$0
`
`
`
`m8,830m820m830m1
`
`2:32wr.bEEBE83359:“;A
`
`
`

`

`U.S. Patent
`
`Mar. 18, 2014
`
`Sheet 6 of 8
`
`US 8,676,538 B2
`
`
`
`JºIIOIQUO O VOA
`
`
`
`
`
`
`
`
`
`
`
`
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 9 of 19 PageID #: 200
`
`9
`
`

`

`U.S. Patent
`
`US 8,676,538 B2
`
`AL CHRI[15)I H
`
`
`
`
`
`
`
`
`
`
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 10 of 19 PageID #: 201
`
`

`

`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 11 of 19 PageID #: 202
`O11e
`aP
`1
`2
`._H
`%
`g
`
`0U
`
`e.
`
`wnmyC
`
`
`mM:sflEmoficmi
`139.:=2:me0280M
`
`
`DMAmzsflmo838-comgo338wEEdwob
`
`A“Pmm_za88:30“8San—:znm“$2234/
`%«D.can
`
`
`ur.ma
`
`mt2w
`
`2mB
`
`#.waEDGE
`
`
`
`HU<Omwfigofiom8m@183mBEEP)?BQZ02>on
`
`a69P009S
`
`D5|6,
`
`ma7onw
`
`n2e9
`
`mm8w
`
`dmbS
`
`mm
`
`.258552:»2:85on7.43:58mat—22£83mHmomegaHo:Ho“8253
`
`
`
`
`
`
`
`
`
`fl0communes:23mm@85835388mm:sfl@85865ZMAmvuouoflmowEEmmoB960A9EwBBmagmachn—@89898EH25EBbEBEdFAQ0262
`
`
`
`

`

`US 8,676,538 B2
`
`1.
`ADJUSTING WEIGHTING OFA
`PARAMETER RELATING TO FAULT
`DETECTION BASED ON ADETECTED
`FAULT
`
`BACKGROUND OF THE INVENTION
`
`10
`
`15
`
`25
`
`30
`
`1. Field of the Invention
`This invention relates generally to semiconductor manu
`facturing, and, more particularly, to a method, system, and
`apparatus for performing a process to improve fault detection
`reliability through feedback.
`2. Description of the Related Art
`The technology explosion in the manufacturing industry
`has resulted in many new and innovative manufacturing pro
`cesses. Today's manufacturing processes, particularly semi
`conductor manufacturing processes, call for a large number
`of important steps. These process steps are usually vital, and
`therefore, require a number of inputs that are generally fine
`tuned to maintain proper manufacturing control.
`The manufacture of semiconductor devices requires a
`number of discrete process steps to create a packaged semi
`conductor device from raw semiconductor material. The vari
`ous processes, from the initial growth of the semiconductor
`material, the slicing of the semiconductor crystal into indi
`vidual wafers, the fabrication stages (etching, doping, ion
`implanting, or the like), to the packaging and final testing of
`the completed device, are so different from one another and
`specialized that the processes may be performed in different
`manufacturing locations that contain different control
`schemes.
`Generally, a set of processing steps is performed across a
`group of semiconductor wafers, sometimes referred to as a
`lot. For example, a process layer that may be composed of a
`variety of different materials may be formed across a semi
`conductor wafer. Thereafter, a patterned layer of photoresist
`may be formed across the process layer using known photo
`lithography techniques. Typically, an etch process is then
`performed across the process layer using the patterned layer
`of photoresist as a mask. This etching process results in the
`formation of various features or objects in the process layer.
`Such features may be used as, for example, a gate electrode
`structure for transistors. Many times, trench isolation struc
`tures are also formed in various regions of the semiconductor
`wafer to create electrically isolated areas across a semicon
`ductor wafer. One example of an isolation structure that can
`be used is a shallow trench isolation (STI) structure.
`The manufacturing tools within a semiconductor manufac
`turing facility typically communicate with a manufacturing
`framework or a network of processing modules. Each manu
`50
`facturing tool is generally connected to an equipment inter
`face. The equipment interface is connected to a machine
`interface to which a manufacturing network is connected,
`thereby facilitating communications between the manufac
`turing 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,
`which can be a Software program that automatically retrieves
`the data needed to execute a manufacturing process.
`FIG. 1 illustrates a typical semiconductor wafer 105. The
`semiconductor wafer 105 typically includes a plurality of
`individual semiconductor die 103 arranged in a grid 150.
`Using known photolithography processes and equipment, a
`patterned layer of photoresist may be formed across one or
`more process layers that are to be patterned. As part of the
`photolithography process, an exposure process is typically
`performed by a stepper on approximately one to four die 103
`
`35
`
`40
`
`45
`
`55
`
`60
`
`65
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 12 of 19 PageID #: 203
`
`2
`locations at a time, depending on the specific photomask
`employed. The patterned photoresist layer can be used as a
`mask during etching processes, wet or dry, performed on the
`underlying layer or layers of material, e.g., a layer of poly
`silicon, metal, or insulating material, to transfer the desired
`pattern to the underlying layer. The patterned layer of photo
`resist is comprised of a plurality of features, e.g., line-type
`features or opening-type features that are to be replicated in
`an underlying process layer.
`When processing semiconductor wafers, various measure
`ments relating to the process results on the semiconductor
`wafers, as well as conditions of the processing tool in which
`the wafers are processed, are acquired and analyzed. The
`analysis is then used to modify Subsequent processes. Turning
`now to FIG. 2, a flow chart depiction of a state-of-the-art
`process flow is illustrated. A processing system may process
`various semiconductor wafers 105 in a lot of wafers (block
`210). Upon processing of the semiconductor wafers 105, the
`processing system may acquire metrology data relating to the
`processing of the semiconductor wafers 105 from selected
`wafers in the lot (block 220). Additionally, the processing
`system may acquire tool state sensor data from the processing
`tool used to process the wafers (block 230). Tool state sensor
`data may include various tool state parameters such as pres
`Sure data, humidity data, temperature data, and the like.
`Based upon the metrology data and the tool state data, the
`processing system may perform fault detection to acquire
`data relating to faults associated with the processing of the
`semiconductor wafers 105 (block 240). Upon detecting vari
`ous faults associated with processing of the semiconductor
`wafers 105, the processing system may perform a principal
`component analysis (“PCA) relating to the faults (block
`250). Principal component analysis (PCA) is a multivariate
`technique that models the correlation structure in the data by
`reducing the dimensionality of the data. The correlation may
`take on various forms, such as correlation of problems with
`the processed wafers with problems in the processing tool.
`The PCA may provide an indication of the types of correc
`tions that may be useful in processing Subsequent semicon
`ductor wafers 105. Upon performing the PCA, the processing
`system may perform Subsequent processes upon the semicon
`ductor wafers 105 with various adjustments being based upon
`the PCA (block 260). The PCA performs an analysis to deter
`mine whether there are abnormal conditions that may exist
`with respect to a tool. Upon detecting any abnormal condi
`tions, various signals may be issued, indicating to the opera
`tors that various faults have been detected.
`One issue associated with state-of-the-art methods
`includes the fact that a determination of what constitutes an
`abnormal correlation may be based upon data used to build a
`fault detection model or a PCA model used to perform the
`fault detection analysis and the PCA. Generally, the abnormal
`conditions detected by performing the PCA may be statisti
`cally different from the data that may have been used to build
`the fault detection or the PCA model. The term “statistically
`different may mean a variety of statistical differences, such
`as differences based upon population mean, variance, etc.
`These abnormal conditions may not be an accurate reflection
`of the true manner of operation in which the tool is perform
`ing. For example, if during the development of the fault
`detection model or the PCA model, the values for a pressure
`sensor were held within Small constraints, larger variations in
`the pressure during the actual processing would generally be
`identified as a significant fault. The problem with this meth
`odology is that if the larger variation of the pressure did not
`result in any negative impact to the material being processed,
`then the fault indication may be false. In other words, if the
`
`

`

`US 8,676,538 B2
`
`3
`larger variation was still Small enough that no significant
`impact to the process was present, a false-positive fault indi
`cation occurs. This false-positive introduces inefficiencies
`and idle times in a manufacturing setting.
`More recently, various efforts have been made to incorpo
`rate weighting schemes into PCA. The weighting schemes
`may provide a significant difference in weight attached to
`various parameters, such as the pressure. However, the prob
`lems associated with the state-of-the-art weighting schemes
`include the fact that prior knowledge is required to assign a
`predetermined weight to a particular parameter. For example,
`prior knowledge may indicate that a smalleramount of weight
`should be assigned to the pressure parameter during the PCA
`analysis relating to a particular process. This would reduce
`false indications due to variations in pressure that may have
`been harmless. However, this methodology can be an ineffi
`cient, cumbersome task and, at best, may involve guess work.
`Furthermore, it may not be readily clear if adjusting the
`weight to particular parameters would result in improved or
`worsened PCA relating to a particular process.
`The present invention is directed to overcoming, or at least
`reducing, the effects of, one or more of the problems set forth
`above.
`
`10
`
`15
`
`4
`between a parameter relating to the fault detection analysis
`and a detected fault. The controller also adjusts a weighting
`associated with the parameter based upon the relationship of
`the parameter to the detected fault.
`In another aspect of the present invention, a system is
`provided for performing a dynamic weighting technique for
`performing fault detection. The system comprises a process
`ing tool communicatively coupled to a controller, a metrology
`tool, and a tool state data sensor unit. The processing tool
`performs a process upon a workpiece. The metrology tool
`acquires metrology data relating to the process performed on
`the workpiece to provide metrology data. The tool state data
`sensor unit acquires tool state data. The controller performs a
`fault detection analysis relating to the processing of the work
`piece to determine a relationship between a parameter relat
`ing to the fault detection analysis and a detected fault. The
`controller also adjusts a weighting associated with the param
`eter based upon the relationship of the parameter to the
`detected fault.
`In yet another aspect of the present invention, a computer
`readable program storage device encoded with instructions is
`provided for performing a dynamic weighting technique for
`performing fault detection. The instructions perform a
`method comprising a processing tool communicatively
`coupled to a controller, a metrology tool, and a tool state data
`sensor unit. The processing tool performs a process upon a
`workpiece. The metrology tool acquires metrology data relat
`ing to the process performed on the workpiece to provide
`metrology data. The tool state data sensor unit acquires tool
`state data. The controller performs a fault detection analysis
`relating to the processing of the workpiece to determine a
`relationship between a parameter relating to the fault detec
`tion analysis and a detected fault. The controller also adjusts
`a weighting associated with the parameter based upon the
`relationship of the parameter to the detected fault.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`The invention may be understood by reference to the fol
`lowing description taken in conjunction with the accompany
`ing drawings, in which like reference numerals identify like
`elements, and in which:
`FIG. 1 is a simplified diagram of a prior art semiconductor
`wafer being processed;
`FIG. 2 illustrates a simplified flowchart depiction of a prior
`art process flow during manufacturing of semiconductor
`wafers;
`FIG.3 provides a block diagram representation of a system
`in accordance with one illustrative embodiment of the present
`invention;
`FIG. 4 illustrates a principal component analysis matrix
`table, which depicts a list of tool state variables being corre
`lated with data relating to various processed semiconductor
`wafers, inaccordance with one illustrative embodiment of the
`present invention;
`FIG. 5 illustrates a more detailed block diagram represen
`tation of a tool state data sensor unit of FIG. 3, in accordance
`with one illustrative embodiment of the present invention;
`FIG. 6 illustrates a more detailed block diagram represen
`tation of a dynamic PCA weighting unit of FIG. 3, in accor
`dance with one illustrative embodiment of the present inven
`tion;
`FIG. 7 illustrates a flowchart depiction of a method in
`accordance with one illustrative embodiment of the present
`invention; and
`FIG. 8 illustrates a more detailed flowchart depiction of a
`method of performing a dynamic PCA weighting process, as
`
`SUMMARY OF THE INVENTION
`
`25
`
`30
`
`35
`
`40
`
`In one aspect of the present invention, various methods are
`disclosed for employing a dynamic weighting technique in
`connection with fault detection analysis. In an illustrative
`embodiment, the method comprises processing a workpiece
`and performing a fault detection analysis relating to the pro
`cessing of the workpiece. The method further comprises
`determining a relationship of a parameter relating to the fault
`detection analysis to a detected fault and adjusting a weight
`ing associated with the parameter based upon the relationship
`of the parameter to the detected fault.
`In another aspect of the present invention, a method is
`provided for performing a dynamic weighting technique for
`performing fault detection. The method comprises processing
`a workpiece and performing a fault detection analysis relating
`to the processing of the workpiece based upon a tool state
`parameter being input into a fault detection model associated
`with the fault detection analysis. The method further com
`prises determining whether said parameter is associated with
`a detected fault as a result of performing the fault detection
`analysis and modifying a weighting of the parameter in the
`fault detection model based upon a determination that the
`parameter is associated with the detected fault.
`In yet another aspect of the present invention, a method is
`provided for performing a dynamic weighting technique for
`performing fault detection. The method comprises processing
`a workpiece and performing a fault detection analysis relating
`to the processing of the workpiece based upon a tool state
`parameter being input into a fault detection model associated
`with the fault detection analysis. The method further com
`55
`prises performing a principal component analysis (PCA) in
`conjunction with the fault detection analysis and determining
`whether the parameter is associated with a detected fault as a
`result of performing the fault detection analysis and the PCA.
`The method further comprises modifying a weighting of the
`parameter in the fault detection model based upon a determi
`nation that the parameter is associated with the detected fault.
`In another aspect of the present invention, an apparatus is
`provided for performing a dynamic weighting technique for
`performing fault detection. The apparatus comprises a con
`troller that performs a fault detection analysis relating to a
`processing of a workpiece to determine a relationship
`
`45
`
`50
`
`60
`
`65
`
`Case 4:20-cv-00991 Document 1-7 Filed 12/31/20 Page 13 of 19 PageID #: 204
`
`

`

`US 8,676,538 B2
`
`5
`indicated in FIG. 7, in accordance with one illustrative
`embodiment of the present invention.
`While the invention is susceptible to various modifications
`and alternative forms, specific embodiments thereof have
`been shown by way of example in the drawings and are 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, equiva
`lents, and alternatives falling within the spirit and scope of the
`invention as defined by the appended claims.
`
`DETAILED DESCRIPTION OF SPECIFIC
`EMBODIMENTS
`
`6
`corrections to those parameters. This may have the effect of
`reducing the number and/or the magnitude of faults caused by
`those parameters. Similarly, over time, the weighting of the
`model parameters may be modified to reduce the frequency of
`false positive fault indications, thereby reducing unnecessary
`downtime and inefficiencies during the manufacturing of
`semiconductor wafers 105.
`Turning now to FIG. 3, a block diagram depiction of a
`system 300 in accordance with illustrative embodiments of
`the present invention is illustrated. A process controller 305 in
`the system 300 is capable of controlling various operations
`relating to a processing tool 310. The process controller 305
`may comprise a computer system that includes a processor,
`memory, and various computer-related peripherals. More
`over, although a single process controller 305 is schemati
`cally depicted in FIG. 3, in practice, the function performed
`by the process controller 305 may be performed by one or
`more computers or workstations spread throughout the manu
`facturing system.
`Semiconductor wafers 105 are processed by the processing
`tool 310 using a plurality of control input signals, or manu
`facturing parameters, provided via a line or network315. The
`control input signals, or manufacturing parameters, on the
`line 315 are sent to the processing tool 310 from a process
`controller 305 via machine interfaces that may be located
`inside or outside the processing tool 310. In one embodiment,
`semiconductor wafers 105 may be provided to the processing
`tool 310 manually. In an alternative embodiment, semicon
`ductor wafers 105 may be provided to the processing tool 310
`in an automatic fashion (e.g., robotic movement of semicon
`ductor wafers 105). In one embodiment, a plurality of semi
`conductor wafers 105 is transported in lots (e.g., stacked in
`cassettes) to the processing tools 310. Examples of the pro
`cessing tool used in semiconductor manufacturing processes
`may be photolithography tools, ion implant tools, steppers,
`etch process tools, deposition tools, chemical-mechanical
`polishing (CMP) tools, and the like.
`The system 300 is capable of acquiring manufacturing
`related data, Such as metrology data, related to processed
`semiconductor wafers 105, tool state data, and the like. The
`system 300 may also comprise a metrology tool 350 to
`acquire metrology data related to the processed semiconduc
`tor wafers 105. The system 300 may also comprise a tool state
`data sensor unit 320 for acquiring tool state data. The tool
`state data may include pressure data, temperature data,
`humidity data, gas flow data, various electrical data, a level of
`out-gas data, and other types of data, related to operations of
`the processing tool 310. Exemplary tool state data for an etch
`tool may include gas flow, chamber pressure, chamber tem
`perature, Voltage, reflected power, backside helium pressure,
`RF tuning parameters, etc. The tool state data may also
`include data external to the processing tool 310. Such as
`ambient temperature, humidity, pressure, etc. A more detailed
`illustration and description of the tool state data sensor unit
`320 is provided in FIG. 5 and accompanying description
`below.
`The system 300 may also comprise a database unit 340.
`The database unit 340 is provided for storing a plurality of
`types of data, such as manufacturing-related data, data related
`to the operation of the system 300 (e.g., the status of the
`processing tool 310, the status of semiconductor wafers 105,
`etc.). The database unit 340 may store parameter data relating
`to parameters used in fault detection and PCA models, as well
`as tool state data relating to a plurality of process runs per
`formed by the processing tool 310. The database unit 340 may
`comprise a database server 342 for storing tool state data
`
`10
`
`15
`
`30
`
`35
`
`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 deci
`sions must be made to achieve the developers specific goals,
`Such as compliance with system-related and business-related
`constraints, which will vary from one implementation to
`another. Moreover, it will be appreciated that such a develop
`ment effort might be complex and time-consuming, but
`25
`would nevertheless be a routine undertaking for those of
`ordinary skill in the art having the benefit of this disclosure.
`There are many discrete processes that are involved in
`semiconductor manufacturing. Many times, workpieces
`(e.g., semiconductor wafers 105, semiconductor devices,
`etc.) are stepped through multiple manufacturing process
`tools. Embodiments of the present invention provide for per
`forming a dynamic adjustment of the weighting of one or
`more parameters associated with fault detection and/or per
`forming a principal component analysis (PCA). The weight
`ing of various parameters that may be used in a fault detection
`model and/or a PCA model may be automatically determined
`and the weighting of the parameters may be adjusted dynami
`cally. For example, after a fault condition is identified by a
`processing system, an automatic input or a manual input may
`40
`be provided to the processing system to indicate whether the
`detected fault was a significant fault or an insignificant fault.
`Based upon this indication, a weighting fault matrix, which
`contains data correlating various tool state parameters to par
`ticular wafers, may be modified to make the detection of
`45
`similar faults more likely, or alternatively, less likely. There
`fore, in multi-variate fault detection and/or PCA models, one
`or more parameters that contributed to the fault condition and
`their relative importance to the fault may be detected and a
`dynamic adjustment of the weighting of those parameters that
`contributed the fault may be increased proportionally. Like
`wise, one or more parameters that did not significantly con
`tribute to the fault condition and their relative non-importance
`to the fault may be characterized and a dynamic adjustment of
`the weighting of those parameters may be decreased propor
`tionally. In other words, the weighting of the parameters that
`were found not to have contributed to a fault may be
`decreased. Therefore, a stronger signal would be required
`relating to those para

This document is available on Docket Alarm but you must sign up to view it.


Or .

Accessing this document will incur an additional charge of $.

After purchase, you can access this document again without charge.

Accept $ Charge
throbber

Still Working On It

This document is taking longer than usual to download. This can happen if we need to contact the court directly to obtain the document and their servers are running slowly.

Give it another minute or two to complete, and then try the refresh button.

throbber

A few More Minutes ... Still Working

It can take up to 5 minutes for us to download a document if the court servers are running slowly.

Thank you for your continued patience.

This document could not be displayed.

We could not find this document within its docket. Please go back to the docket page and check the link. If that does not work, go back to the docket and refresh it to pull the newest information.

Your account does not support viewing this document.

You need a Paid Account to view this document. Click here to change your account type.

Your account does not support viewing this document.

Set your membership status to view this document.

With a Docket Alarm membership, you'll get a whole lot more, including:

  • Up-to-date information for this case.
  • Email alerts whenever there is an update.
  • Full text search for other cases.
  • Get email alerts whenever a new case matches your search.

Become a Member

One Moment Please

The filing “” is large (MB) and is being downloaded.

Please refresh this page in a few minutes to see if the filing has been downloaded. The filing will also be emailed to you when the download completes.

Your document is on its way!

If you do not receive the document in five minutes, contact support at support@docketalarm.com.

Sealed Document

We are unable to display this document, it may be under a court ordered seal.

If you have proper credentials to access the file, you may proceed directly to the court's system using your government issued username and password.


Access Government Site

We are redirecting you
to a mobile optimized page.





Document Unreadable or Corrupt

Refresh this Document
Go to the Docket

We are unable to display this document.

Refresh this Document
Go to the Docket