`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 1 of 12 PageID #: 313
`
`EXHIBIT O
`
`EXHIBIT 0
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 2 of 12 PageID #: 314
`
`Analysis of Infringement of U.S. Patent No. 6,836,691 by Huawei Device USA Inc., Huawei Device Co., Ltd., and HiSilicon Technologies Co., Ltd.
` (Based on Public Information Only)
`
`Plaintiff Ocean Semiconductor LLC (“Ocean Semiconductor”), provides this preliminary and exemplary infringement analysis with respect to
`
`infringement of U.S. Patent No. 6,836,691, entitled “METHOD AND APPARATUS FOR FILTERING METROLOGY DATA BASED ON COLLECTION
`PURPOSE” (the “’691 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, and integrated
`circuits, and products containing such circuits, fabricated or manufactured 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 “’691 Infringing Instrumentalities”).
`
`The analysis set forth below is based only upon information from publicly available resources regarding the ’691 Infringing Instrumentalities, as
`Huawei has not yet provided any non-public information.
`
`Unless otherwise noted, Ocean Semiconductor contends that Huawei directly infringes the ’691 patent in violation of 35 U.S.C. § 271(g) by using,
`
`selling, and/or offering to sell in the United States, and/or importing into the United States, the ’691 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.
`
`Unless otherwise noted, Ocean Semiconductor believes and contends that each element of each claim asserted herein is literally met through Huawei’s
`
`provision or importation of the ’691 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 ’691 Infringing Instrumentalities, Ocean Semiconductor did not identify any substantial differences between the elements of the patent
`claims and the corresponding features of the Infringing Instrumentalities, as set forth herein. In each instance, the identified feature of the ’691 Infringing
`
`
`
`1
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 3 of 12 PageID #: 315
`
`Instrumentalities performs at least substantially the same function in substantially the same way to achieve substantially the same result as the corresponding
`claim element.
`
`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 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.
`
`
`
`
`
`
`
`2
`
`
`
`USP No. 6,836,691
`
`1. A method, comprising:
`collecting metrology data
`related to the processing of
`workpieces in a plurality of
`tools;
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 4 of 12 PageID #: 316
`
`
`
`Infringement by the ’691 Accused Instrumentalities
`
`PDF Solutions Inc.’s Exensio platform (the “Exensio platform”) collects metrology data related to the processing of workpieces in a
`plurality of tools.
`
`For example, the Exensio platform enables links across fab, test floor, and other enterprise data types including “inline and end-of-line
`metrology, yield, parametric, performance, manufacturing consumables, tool-level sensor data, test floor data, logistical data, as well as
`custom data types. By providing a common environment for all these different data types from many different points in the manufacturing
`and test process,” the Exensio platform is “designed to enable customers to rapidly perform root cause diagnosis of yield, performance, and
`quality issues that impact manufacturing and test operations,” 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 Apr. 30, 2020) (“2020 Form 10-K”).
`
`The Exensio platform also collects metrology data to determine reliability risk and early life failure:
`
`
`
`
`
`
`
`
`3
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 5 of 12 PageID #: 317
`
`See S1.2—Exensio Platform, 16th Annual PDF Solutions Users Conference (Oct. 15, 2019) at 6, 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).
`
`The metrology data is collected, analyzed, and controlled across the entire manufacture pipeline, including integrated circuit design,
`fabrication, sort, assembly, test, and system:
`
`
`
`See S1.2—Exensio Platform Presentation at 11 (annotated).
`
`The Exensio Platform collects the metrology data through a number of its platform modules, including the control module (to detect and
`identify process or tool problems in fab and assembly in real time), the test module (to prevent test issues and offer higher yield and
`reliability), the yield module (to drive higher manufacturing yields and integrate all frontend and backend data), the char module (to
`provide big data analytics on processing tools), and the ALPS module (to trace wafers, dies, and multichip modules):
`
`
`
`
`
`
`4
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 6 of 12 PageID #: 318
`
`
`
`
`See PDF Solutions, Inc. Overview Jefferies Conferences (Aug. 28-29, 2018) at 9, available at
`https://www.pdf.com/upload/File/Investors/PDFExecOveriew2018II.pdf (last visited Apr. 30, 2020) (“PDF Overview”) (annotated).
`
`
`PDF’s Exensio platform generates context data for the metrology data, the context data including collection purpose data.
`
`For example, the Exensio platform generates context data including collection purpose data covering process control, test operations,
`manufacturing analytics, assembly operations, and process characterization:
`
`
`generating context data for
`the metrology data, the
`context data including
`collection purpose data;
`
`
`
`5
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 7 of 12 PageID #: 319
`
`
`See S1.2—Exensio Platform Presentation at 3; see also id. at 4 (annotated):
`
`
`
`
`
`
`
`
`The collection purpose data, for example, includes:
`
`
`
`6
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 8 of 12 PageID #: 320
`
`• Material descriptions (e.g., lot #, wafer #, die);
`• Meta data (e.g., recipe data/time, process flow, stages, and steps);
`• Fault detection and control (e.g., trace charts, model prediction, real time data collection on defects);
`• Defect & metrology (e.g., lot/wafer summaries, defect summary, kill ratio, and defect images); and
`• Assembly system (e.g., location of reel/tube, die traceability, and equipment parameters):
`
`
`
`
`
`filtering the metrology data
`based on the collection
`purpose data; and
`
`
`
`
`See S1.2—Exensio Platform Presentation at 11 (annotated).
`
`PDF’s Exensio platform filters the metrology data based on the collection purpose data.
`
`As an example, the Exensio platform uses semantic modeling to filter the metrology data (e.g., by cleaning, aligning, and interpreting the
`data) to address, for example, a particular process control activity (e.g., aligning events in fabrication with wafer data to answer process-
`related questions such as “which wafers were processed with the new batch of resist”):
`
`
`
`
`7
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 9 of 12 PageID #: 321
`
`
`See S1.2—Exensio Platform Presentation at 10 (annotated).
`
`As another example, the Exensio platform uses machine learning and multiple algorithms to filter the metrology data based on the
`collection purpose data:
`
`
`
`
`
`
`
`
`
`
`8
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 10 of 12 PageID #: 322
`
`conducting a process
`control activity related to
`one of the tools based on
`the filtered metrology data.
`
`See S1.2—Exensio Platform Presentation at 6 (annotated).
`
`PDF’s Exensio platform conducts a process control activity related to one of the tools based on the filtered metrology data.
`
`For example, based on the filtered metrology data, the Exensio platform is able to detect early life failure of a particular die or chipset
`package and determine whether to downgrade or scrap the die or chipset package:
`
`
`
`See S1.2—Exensio Platform Presentation at 6 (annotated).
`
`As another example, based on the filtered metrology data, the Exensio platform identifies losses due to problems in fabrication, test and
`design, which in turn, allow quick actions to be taken to improve key performance metrics, including achieving and more stable yields,
`reducing scraps, allowing more consistent and optimized test, and increasing engineering productivity:
`
`
`
`
`
`
`9
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 11 of 12 PageID #: 323
`
`
`See Exensio-Yield, Rich Semiconductor Capabilities Delivered on an Easy-to-Use Analytics Platform, available at
`http://www.pdf.com/Exensio-Yield
`(last visited Apr. 30, 2020).
`
`As another example, the Exensio platform identifies invisible defects, traces components during assembly and packaging, and optimizes
`system performance across supply chain based on the filtered metrology data:
`
`
`
`
`
`
`
`
`
`
`10
`
`
`
`Case 4:20-cv-00991-ALM Document 1-15 Filed 12/31/20 Page 12 of 12 PageID #: 324
`
`See Kibarian et al., PDF Solutions, Inc. Needham Growth Conference (Jan. 16, 2019) at 5, available at
`https://www.pdf.com/upload/File/Investors/INVPres2019/PDFS%20investor%20presentation%2016-Jan-2019%20(final).pdf (last visited
`Apr. 30, 2020) (“PDF Needham Conference Presentation”).
`
`As yet another example, based on the filtered metrology data, the Exensio platform controls activities in fault-detection and classification,
`testing, assembly and packaging, and data characterization:
`
`
`
`
`
`See S1.2—Exensio Platform Presentation at 12 (annotated).
`
`As yet another example, based on the filtered metrology data, the Exensio platform monitors, triggers alarms, and controls manufacturing
`tool sets:
`
`“● 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.”
`
`See 2020 Form 10-K at 7.
`
`
`
`
`
`11
`
`