Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 1 of 9 PageID #: 337
`Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 1 of 9 PageID #: 337
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`EXHIBIT Q
`EXHIBIT Q
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`Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 2 of 9 PageID #: 338
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`Analysis of Infringement of U.S. Patent No. 8,676,538 by Huawei Device USA Inc., Huawei Device Co., Ltd., and HiSilicon Technologies Co., Ltd.
`(Based on Public Information Only)
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`Plaintiff Ocean Semiconductor LLC (“Ocean Semiconductor”), provides this preliminary and exemplary infringement analysis with respect to infringement of U.S.
`
`Patent No. 8,676,538, entitled “ADJUSTING WEIGHTING OF A PARAMETER READING TO A FAULT DETECTION BASED ON A DETECTED FAULT” (the “’538
`patent) by Huawei Device USA Inc., Huawei Device Co., Ltd., and HiSilicon Technologies Co., Ltd. (“Huawei”). The following chart illustrates an exemplary analysis
`regarding infringement by Defendant Huawei’s semiconductor products, systems, devices, components, integrated circuits, and products containing such circuits, fabricated or
`using PDF Solutions, Inc.’s (“PDF Solutions”) platforms, and/or framework, including PDF Solutions’ software and APC system, including the Exensio platform hardware
`and/or software (collectively, “Exensio”) and/or other APC system and platform hardware and/or software. Such products include, without limitation, SoC chipsets and
`solutions (e.g., Hi3559A V100, Hi3519A V100, Hi3516D V300, Hi3556A V100, Hi3559 V200, Hi3559A V100, Hi3559C V100, Hi3559 V100, Hi3716M V430, Hi3716M
`V430, Hi3798C V200, Hi3798M V200H, Hi3798M V300, Hi3798M V310, Hi3796M V200, Hi3798M V200, Hi3796M V100, Hi3798M V100, Hi3716M V420, Hi3716M
`V410, and Hi3751 V553), processors (e.g., Hi3536, Hi3536C, Hi3536D V100, Hi3531D V100, Hi3521D V100, Hi3520D V400, Hi3520D V300, and Hi3520D V200), TV
`solutions (e.g., Hi3731 V201, Hi3731 V101, Hi3751 V811, HI3751 V810, Hi3751 V551, Hi3751 V730, Hi3751 V620, Hi3751 V510, Hi3751 V310, Hi3751 V320, and
`Hi3751 V600), Kirin solutions (e.g., Kirin 9000/E, Kirin 1020, Kirin 990, Kirin 980, Kirin 970, Kirin 960, Kirin 950, Kirin 930, Kirin 920, Kirin 910, and Kirin 710); Ascend
`solutions (e.g., Ascend 310 and Ascend 910); Kunpeng solutions (e.g., Kunpeng 920); and Balong solutions (e.g., Balong 5000, Balong 5G01, Balong 765, Balong 750,
`Balong 720, Balong 710, and Balong 700), systems, products, or devices containing these solutions, and similar systems, products, devices, and integrated circuits
`(collectively, the “’538 Infringing Instrumentalities”).
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`The analysis set forth below is based only upon information from publicly available resources regarding the ’538 Infringing Instrumentalities, as Huawei has not yet
`provided any non-public information.
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`Unless otherwise noted, Ocean Semiconductor contends that Huawei directly infringes the ’538 patent in violation of 35 U.S.C. § 271(g) by using, selling, and/or
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`offering to sell in the United States, and/or importing into the United States, the ’538 Infringing Instrumentalities. The following exemplary analysis demonstrates that
`infringement. Unless otherwise noted, Ocean Semiconductor further contends that the evidence below supports a finding of indirect infringement under 35 U.S.C. § 271(b) in
`conjunction with other evidence of liability.
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`Unless otherwise noted, Ocean Semiconductor believes and contends that each element of each claim asserted herein is literally met through Huawei’s provision or
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`importation of the ’538 Infringing Instrumentalities. However, to the extent that Huawei attempts to allege that any asserted claim element is not literally met, Ocean
`Semiconductor believes and contends that such elements are met under the doctrine of equivalents. More specifically, in its investigation and analysis of the ’538 Infringing
`Instrumentalities, Ocean Semiconductor did not identify any substantial differences between the elements of the patent claims and the corresponding features of the ’538
`Infringing Instrumentalities, as set forth herein. In each instance, the identified feature of the ’538 Infringing Instrumentalities performs at least substantially the same
`function in substantially the same way to achieve substantially the same result as the corresponding claim element.
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`Ocean Semiconductor notes that the present claim chart and analysis are necessarily preliminary in that Ocean Semiconductor has not obtained substantial discovery
`from Huawei nor has Huawei disclosed any detailed analysis for its non-infringement position, if any. Further, Ocean Semiconductor does not have the benefit of claim
`construction or expert discovery. Ocean Semiconductor reserves the right to supplement and/or amend the positions taken in this preliminary and exemplary infringement
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`Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 3 of 9 PageID #: 339
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`analysis, including with respect to literal infringement and infringement under the doctrine of equivalents, if and when warranted by further information obtained by Ocean
`Semiconductor, including but not limited to information adduced through information exchanges between the parties, fact discovery, claim construction, expert discovery,
`and/or further analysis.
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`Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 4 of 9 PageID #: 340
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`USP 8,676,538
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`Infringement by the ’538 Accused Instrumentalities
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`1. A method comprising:
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`performing in a computer a
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`fault detection analysis relating
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`to a processing of a workpiece;
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`determining in a said computer
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`a relationship of a parameter
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`relating to said fault detection
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`analysis to a detected fault;
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`To the extent that the preamble of Claim 1 is a limitation, PDF Solutions’ Exensio performs in a computer a fault detection analysis relating
`to a processing of a workpiece.
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`For example, Exensio includes a software module for Fault Detection and Classification (FDC), as shown below:
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`“Exensio –Control is a scalable Fault Detection and Classification (FDC) software solution that controls semiconductor manufacturing
`equipment and processes. Exensio-Control allows manufacturers to accurately detect and identify process or tool problems that arise during
`production, in real-time.
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`• Wide data acquisition capabilities — Exensio –Control acquires all the equipment and logistics data for FDC analysis, in any format
`and from any source (Interface A, databases, SECS/HSMS, automation, files, etc.)
`• Advanced analysis capabilities — Exensio –Control includes signal transformation and summarization, univariate SPC, multivariate
`fault detection and classification functions, plus meta-analysis based on indicators. In addition, Exensio –Control provides off-line
`and historical analysis capabilities to test FDC control strategies before deployment.
`• Real-time alarms and events management — Exensio –Control centralizes and assesses events and alarms to trigger the appropriate
`action. Equipment alarms and events are overlaid with trace and univariate SPC charts and can be analyzed in conjunction with FDC
`alarms. … ”
`
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`See Exensio Control, available at http://www.pdf.com/Exensio-Control (last visited Oct. 12, 2020).
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`As a further example, the Exensio platform is “designed to enable real-time rapid diagnosis and understanding of key manufacturing and test
`metrics during both inline and end-of-line wafer processing,” and “enable[s] predictive and proactive optimization decisions for process
`control, process adjustments, PM scheduling, tool corrective actions, wafer dispatching, and wafer level and final test.” See PDF Solutions
`Inc.’s Form 10-K (filed Mar. 10, 2020) at 6, available at http://ir.pdf.com/static-files/fb23407a-dfbc-489f-adb1-ac54e83102ad (last visited
`Oct. 12, 2020) (“2020 Form 10-K”).
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`Exensio determines in the computer a relationship of a parameter relating to said fault detection analysis to a detected fault.
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`As an example, the Exensio System is adapted to receive monitor and identify a fault condition covering various process parameters of the
`processing tool:
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`“● Exensio Control – This software provides failure detection and classification (or FDC) capabilities for monitoring, alarming and control of
`manufacturing tool sets. These capabilities include proprietary data collection and analysis of tool sensor trace data and summary indicators
`designed to rapidly identify sources of process variations and manufacturing excursions. When used together with Exensio Yield and related
`modules, the accretive data mining and correlation capabilities are designed to enable identification of tool level sources of yield loss and
`process variation that impact end of line product yield, performance and reliability.”
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`Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 5 of 9 PageID #: 341
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`See 2020 Form 10-K at 7.
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`As a further example, Exensio collects data related to, e.g., equipment parameters, as shown below:
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`
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`See S1.2—Exensio Platform, 16th Annual PDF Solutions Users Conference (Oct. 15, 2019) at 11, available at
`http://www.pdf.com/upload/File/Investors/PUG2019/S1.2%20PUG2019_ExensioPlatform_SaidAkar.pdf (“S1.2—Exensio Platform
`Presentation”) (last visited Apr. 30, 2020) (annotated).
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`As a further example, Exensio uses algorithms and a Single Machine Learning Pipeline to model processes for fault detection and
`classification, as shown below:
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`See S1.3— Machine Learning in Exensio, 16th Annual PDF Solutions Users Conference (Oct. 15, 2019) at 23, available at
`http://www.pdf.com/upload/File/Investors/PUG2019/S1.3%20PUG2019_AISolutions_JeffDavid.pdf (“S1.3—Machine Learning in
`Exensio”) (last visited Oct. 12, 2020).
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`As a further example, the Maestria FDC platform produced by PDF Solutions performs fault detection and classification using univariate and
`multivariate models, as shown below:
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`Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 7 of 9 PageID #: 343
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`See “Usage of Hercules inside commercial FDC System Maestria,” (“Hercules Maestria Presentation”) at 11, available at
`https://www.plasmetrex.com/ref/workshop/2007/foeh.pdf (last visited Oct. 12, 2020).
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`Exensio adjusts in the computer a weighting of said parameter based upon said relationship of said parameter to said detected fault.
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`For example, Exensio uses “[o]ne-step parameter screening with advanced diagnostics to drive response correlations to feedback to tool
`control (dynamic SPC) for yield, device parametrics, and metrology”
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`See https://www.pdf.com/products/exensio-analytics-platform/modules/process-control/ (last visited Oct. 12, 2020).
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`As a further example, Exensio performs “[a]utomated detection of excursion events with FDC tool sensor level diagnostics and drilldown to
`perform process and metrology shifts, parametric drift, preventative maintenance and consumable event detection.”
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`See id.
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`As a further example, Exensio “[i]dentifies what is a nuisance defect and what is a critical defect and adjust[s] accordingly,” as shown below:
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`“Find the Source of Critical Defects Quickly and Easily
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`adjusting in said computer a
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`weighting of said parameter
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`based upon said relationship of
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`said parameter to said detected
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`fault; and
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`Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 8 of 9 PageID #: 344
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`Exensio Foundry collects all fab sensor data together in a common semantic database making it simple to connect data together to drive
`actionable insights. Defect wafer maps from the fab can be linked to actual defect images. Identify which equipment causes more defects.
`Identify what is a nuisance defect and what is a critical defect and adjust sensitivity accordingly. That is the power of Exensio Foundry.”
`
`See https://www.pdf.com/products/exensio-analytics-platform/products/exensio-foundry/ (last visited Oct. 12, 2020).
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`As a further example, one of PDF Solutions’ own patents discloses “adjust[ing] the process parameters of the established fabrication process”
`in response to the identification of systematic defects. See U.S Patent 7,739,065 at 4:65-5:5.
`
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`Exensio performs in the computer fault detection analysis relating to processing of a subsequent workpiece using adjusted weighting.
`performing in said computer the
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`For example, Exensio’s FDC analysis uses “comparison and analysis to a golden tool or known good tool/chambers,” as shown below:
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`fault detection analysis relating
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`to processing of a subsequent
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`workpiece using said adjusted
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`weighting.
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`See https://www.pdf.com/products/exensio-analytics-platform/modules/process-control/ (last visited Oct. 12, 2020).
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`As another example, the Exensio System adjusts processing of the processing tool by updating recipe tables and processing/tool parameters in
`response to the presence of a fault condition:
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`Case 4:20-cv-00991-ALM Document 1-17 Filed 12/31/20 Page 9 of 9 PageID #: 345
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`See PDF Solutions, “Cognitive End to End Analytics for Semiconductor Manufacturing: A Smart Testing Application” (Oct. 30, 2019) at 20,
`available at http://liralingerie.com/nldfpd/end-to-end-analytics.html (Last visited Apr. 30, 2020) (“Cognitive End to End Analytics
`Presentation”) (annotated).
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`As a further example, the Maestria FDC system also produced by PDF Solutions “focuses on real-time (on-line) detection of manufacturing
`equipment parameter excusion,” as shown below:
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`“The PDF Solutions maestria® FDC tool focuses on real-time (on-line) detection of manufacturing equipment parameter excursion and also
`provides valuable data for further analysis. The data is collected, analyzed and stored in a mixed architecture: distributed for local efficient
`fault detection, and centralized for fab wide data consolidation and correlation with other product data.”
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`See “Bosch Selects FDC Solution from PDF Solutions,” available at https://www.nbcnews.com/id/wbna40698254 (last visited Oct. 12,
`2020). See also maestria® Support & Maintenance Terms, available at https://www.pdf.com/wp-content/uploads/2020/03/maestria-
`Maintenance-and-Support-2008-0731.pdf (last visited Oct. 12, 2020).
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