Case 4:20-cv-00991-ALM Document 1-16 Filed 12/31/20 Page 1 of 12 PageID #: 325
`Case 4:20-cv-00991-ALM Document 1-16 Filed 12/31/20 Page 1 of 12 PageID #: 325
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`EXHIBIT P
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`EXHIBIT P
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`Case 4:20-cv-00991-ALM Document 1-16 Filed 12/31/20 Page 2 of 12 PageID #: 326
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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)
`
`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 manufactured using Applied Materials, Inc.’s (“Applied Materials”) platforms, and/or
`framework, including Applied Materials’ software and APC system, including the E3 platform hardware and/or software (collectively, “E3”) 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”).
`
`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.
`
`Unless otherwise noted, Ocean Semiconductor contends that Huawei directly infringes the ’538 patent in violation of 35 U.S.C. § 271(g) by using,
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`selling, and/or 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.
`
`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 ’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
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`Case 4:20-cv-00991-ALM Document 1-16 Filed 12/31/20 Page 3 of 12 PageID #: 327
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`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 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 1-16 Filed 12/31/20 Page 4 of 12 PageID #: 328
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`USP 8,676,538
`
`Infringement by the ’538 Accused Instrumentalities
`
`1. A method comprising:
`performing in a computer a
`fault detection analysis
`relating to a processing of a
`workpiece;
`
`
`To the extent that the preamble of Claim 1 is a limitation, the Applied Materials E3 system performs in a computer a fault
`detection analysis relating to a processing of a workpiece.
`
`For example, the Applied Materials E3 (e.g., including its Fault Detection and Classification (“FDC”) module and Run-to-Run
`(“R2R”) module) performs in a computer a fault detection analysis relating to a processing of a wafer, as shown below:
`
`“The Applied E3 FDC module is the only fault detection and analysis solution in the market today built on a common platform
`with integration to statistical process control (SPC), equipment performance tracking (EPT), run to run (R2R) control and
`advanced data mining (ADM). The FDC module continuously monitors equipment sensors and events against performance
`metrics using statistical analysis techniques, and provides proactive and rapid feedback on equipment health. Using the E3 FDC
`module, engineers can analyze sensor data from manufacturing equipment, detect out-of-norm conditions and relate them to
`problems with tools.”
`
`See Applied E3 FDC Datasheet, available at
`http://www.appliedmaterials.com/files/E3FDCDatasheet.pdf (last visited Oct. 12, 2020).
`
`As a further example, Applied E3 is a computer software package, as shown below:
`
`“Applied Materials, Inc. today announced its Applied E3™ advanced equipment and process control solution, a comprehensive
`factory automation (FA) software package for improving the productivity and reducing the costs of semiconductor, flat panel
`display and photovoltaic solar cell manufacturing.”
`
`See “Applied Materials Launches Breakthrough E3 Equipment and Process Control Solution for Boosting Fab Productivity” (“E3
`Press Release”), available at https://www.appliedmaterials.com/en-in/company/news/press-releases/2008/07/applied-materials-
`launches-breakthrough-e3-equipment-and-process-control-solution-for-boosting-fab-productivity (last visited Oct. 12, 2020).
`
`As a further example, within an Advanced Processing Control (“APC”) system such as Applied E3, fault detection is understood
`as “[t]he technique of monitoring and analyzing variations in tool and/or process data to detect anomalies.”
`
`See James Moyne and Jimmy Iskandar, “Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor
`Manufacturing,” 5 Processes 20 (2015), available at https://www.mdpi.com/2227-9717/5/3/39 (last visited Oct. 12, 2020).
`
`As a further example, E3 performs a fault detection analysis relating to a workpiece, e.g. a wafer:
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`
`“Predict and Prevent. The E3 FDC solution gives process engineers the flexibility to not only perform corrective maintenance, but
`to also predict and proactively schedule a system for repair before a failure can occur. For example, when data exists for both a
`known good substrate (e.g., wafer or glass) and a known bad substrate, sensor traces can be superimposed to help identify a
`potential root cause. Using this type of data-driven troubleshooting approach, predictability of operations increases and tool
`downtime and unnecessary parts replacements can be significantly reduced.”
`
`See Applied E3 FDC Datasheet.
`
`
`
`determining in a said
`computer a relationship of a
`parameter relating to said
`fault detection analysis to a
`detected fault;
`
`Applied E3 determines in the computer a relationship of a parameter relating to said fault detection analysis to a detected fault.
`
`For example, the E3 analyzes equipment parameters based on data collection and logic handling to determine a relationship of a
`parameter relating to the fault detection analysis to a detected fault, as shown below:
`
`“Using an advanced, scalable software architecture, the Applied E3 solution provides a powerful combination of modules.
`Equipment automation, data collection and logic handling simplify the construction, deployment, and maintenance of automated
`process control (APC) applications. Fault detection and classification (FDC) collects and analyzes equipment parameters to
`provide rapid feedback on process performance issues and avoid unexpected failures that decrease productivity. Run-to-run
`control (R2R) uses patented feedback algorithms to reduce process variability by adjusting processing parameters in real time,
`enabling more consistent output, higher yield and greater productivity. Equipment performance tracking (EPT) monitors every
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`processing tool in the factory and provides visual and statistical reporting tools to identify bottlenecks and improve factory
`performance.”
`
`See “E3 Press Release at 1.
`
`See also “Applied SmartFactory Fault Detection and Classification,” available at https://www.appliedmaterials.com/automation-
`software/e3-fault-detection-and-classification-fdc (last visited Oct. 12, 2020) (annotated):
`
`
`
`See also “Advanced Data Mining Techniques to Improve IC Fab Yield,” available at
`
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`https://www.appliedmaterials.com/nanochip/nanochip-fab-solutions/december-2014/data-mining-techniques (last visited Oct. 12,
`2020):
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`adjusting in said computer a
`weighting of said parameter
`based upon said relationship
`of said parameter to said
`detected fault; and
`
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`Case 4:20-cv-00991-ALM Document 1-16 Filed 12/31/20 Page 8 of 12 PageID #: 332
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`
`See also id. (“Next, a model quality report is generated with the top-ranked variables and their respective contributions (see figure
`2). A plot of predicted values vs. actual values (see figure 3) indicates model quality. A high R-squared value, however, may not
`always indicate the best model fit. Overfitting is not uncommon and should be avoided because it can cost the model its
`generalizability. Validation is then performed to correct for all hardware-, process- and sequence-related changes required to
`solve the yield/output issues and also to match chamber-to-chamber performance. Finally, yield-driven control limits are
`determined and set for each sensor of interest, including derived sensors, and are subsequently monitored for any abnormal
`behavior.”).
`
`
`Applied E3 adjusts in the computer a weighting of said parameter based upon said relationship of said parameter to said detected
`fault.
`
`For example, the E3 “uses patented feedback algorithms to reduce process variability by adjusting processing parameters in real
`time, enabling more consistent output, higher yield and greater productivity.”
`
`See E3 Press Release.
`
`As a further example, the E3 allows “automatic[] . . . adjustments to a process” and “uses metrology data taken at each process
`step to adjust process recipes,” as shown below:
`
`“The Applied E3 R2R control module is the only R2R system built on a common platform with
`integration to statistical process control (SPC), fault detection and classification (FDC), equipment
`performance tracking (EPT) and advanced data mining (ADM) systems. The module gives process
`engineers the ability to automatically make adjustments to a process in order to maintain specific
`properties of the product (for example, wafer thickness or critical dimension) at a required target
`value. It uses metrology data taken at each process step to adjust process recipes on a
`run-to-run basis. In addition, integrating with FDC and SPC allows the controller business rules to
`accommodate process and material excursions seamlessly.”
`
`See Applied E3 R2R Datasheet, available at https://www.appliedmaterials.com/files/E3R2RDatasheet.pdf (last visited Oct. 12,
`2020).
`
`As a further example, the E3 R2R “optimiz[es] recipe parameters from lot-to-lot or wafer-to-wafer based on feedback from
`process models,” as shown below:
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`“Automatic Recipe Tuning. The R2R control module improves processing performance and
`reduces process variability by optimizing recipe parameters from lot-to-lot or wafer-to-wafer based
`on feedback from process models, incoming variations and metrology. Available at the tool or
`chamber level, R2R allows customized strategies to be performed in a highly automated fashion.
`R2R enables lower cost of ownership (CoO) by reducing model management activities through a
`unified modeling structure approach and advanced patented technology. This technology supports
`high mix, high complexity manufacturing operations and accommodates missing and out-of-order
`metrology data.”
`
`See id. at 1.
`
`See also “Nanochip Fab Solutions: Data Analytics: Finding What Matters” (V9, Issue 2, 2014), available at
`https://www.appliedmaterials.com/files/nanochip-journals/nanochip-fab-solutions-12-2014-revised.pdf (last visited Oct. 12,
`2020):
`
`“After equipment/chamber PM: In model (1.1), only the equipment offset will be changed after PM. One pilot run may be
`performed to estimate this step change. One way to perform this estimation is to decrease the weighting of the equipment offset
`while keeping all other weights high, and performing state estimation on pilot run only.”
`
`See also “Improving Yield with Fleet Chamber Matching,” available at https://www.appliedmaterials.com/ko/node/3341385
`(last visited Oct. 12, 2020):
`
`“Depending on the matching goals, this target R2R control recipe can take many forms, including (1) a baseline recipe for a
`golden tool, (2) the latest R2R control recipe for that golden tool, and (3) a weighted “average” control recipe across the fleet of
`tools. The latter can be determined from an averaging of R2R recipe advices or an inversion of an average model across the fleet
`of chambers. When recipe advice is requested from a particular chamber, the E3 R2R controller picks a recipe that is closer to the
`target R2R control recipe (among an infinite set of choices), as shown in figure 3. The target R2R control recipe is updated as
`necessary. Relative weighting of variables (among inputs, and between inputs and outputs) can be used to skew the matching
`process toward variables that are determined to be more important to yield matching.”
`
`See also id. (“In this example we are controlling a single output, e.g., thickness, by tuning two input variables: power and
`pressure. We have a simple linear model of the chamber and a current operating point (top graph, red line and dot). With
`traditional R2R control, after a run, the R2R controller identifies a difference between the predicted output and actual output,
`adjusts the model accordingly, and selects a new operating point that is closest to the previous one (orange line and dot). In
`chamber-matched control with a fleet of two chambers (bottom graph), we are aware of the model and operating point for
`chamber 2 (blue line and dot). In this case, after updating the model for chamber 1, we choose an operation point (among an
`
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`infinite set of choices on the line) that is closer to the operating point of chamber 2 (orange line and green dot).”).
`
`As another example, Applied Materials discloses, in one of its issued patents, the use of weighting in fault detection and
`classification:
`
`“When new fault detection and classification data and/or a new yield prediction is received, the factory R2R control module 520
`can adjust high level parameters to improve the predicted yield. These adjustments may modify targets and/or settings of one or
`more inter-process level control modules (e.g., the uniformity R2R control module 525, the CD R2R control module 530, etc.)
`and/or process level control modules (e.g., the deposition R2R control module 535, the CMP R2R control module 540, the
`lithography R2R control module 545, the etch R2R control module 550, etc.). In turn, the inter-process level control modules can
`adjust parameters to coincide with new targets and/or settings provided by the factory R2R control module 520, which may cause
`further changes to targets and/or settings of the process level control modules. The process level R2R control modules may then
`adjust parameters of individual recipes, manufacturing machines, etc. in response to the new targets and settings. For example,
`the deposition R2R control module 535 may adjust parameters of one or more deposition manufacturing machines, the CMP R2R
`control module 540 may adjust parameters of one or more CMP manufacturing machines 560, etc. In one embodiment, an inter-
`process level uniformity R2R control module 525 controls uniformity between CMP and etch processes. The uniformity R2R
`control module 525 adjusts CD targets, controller gains, and weighting of objective parameters (outputs) of the CMP R2R control
`module 540 and the etch R2R control module 550 so as to control post-etch CD. The uniformity R2R control module 525
`receives one or more actions caused by a predicted yield excursion event that predicts that post-lithography there will be a yield
`issue due to lack of CD uniformity. The uniformity R2R control module 525 adjusts the targeting and weighting of the uniformity
`objective for the subsequent etch R2R control module 550. In response, the etch R2R control module 550 adjusts recipe
`parameters on an etch machine 570 to bring them in line with the new targeting and weighting, thus preventing yield loss due to
`CD non-uniformity.”
`
`See U.S. Patent No. 7,974,723, at 15:3-39.
`
`Applied E3 performs in the computer fault detection analysis relating to processing of a subsequent workpiece using adjusted
`weighting.
`
`For example, E3 performs in the computer fault detection analysis relating to processing of a subsequent workpiece using
`adjusted weighting, as shown below:
`
`“Predict and Prevent. The E3 FDC solution gives process engineers the flexibility to not only perform corrective maintenance, but
`to also predict and proactively schedule a system for repair before a failure can occur. For example, when data exists for both a
`known good substrate (e.g., wafer or glass) and a known bad substrate, sensor traces can be superimposed to help identify a
`potential root cause. Using this type of data-driven troubleshooting approach, predictability of operations increases and tool
`
`performing in said computer
`the fault detection analysis
`relating to processing of a
`subsequent workpiece using
`said adjusted weighting.
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`downtime and unnecessary parts replacements can be significantly reduced.”
`
`See Applied E3 FDC Datasheet.
`
`As a further example, using the adjusted weighting, Applied E3 FDC diagnoses a fault condition and implement measures to
`reduce unscheduled downtime and product scrap, as shown below:
`
`“Detect and Diagnose. Engineers can construct classification models to define root cause based on fault detection alarms with the
`E3 FDC strategy engine. This strategy engine provides a dashboard with extensive tools for analyzing various data sources. With
`the dashboard, engineers can drag and drop data collections into data views, reuse previous analysis templates, access all types of
`data in the repository and add comments to run data. The FDC solution also provides a vast library of univariate and multivariate
`analysis tools for developing detailed diagnostic models. These models can detect problems with equipment and provide
`predictive maintenance capabilities that reduce unscheduled downtime and product scrap. The strategy engine also includes
`support for limits management and offers extensive data filtering capabilities to eliminate false positives.”
`
`See id.
`
`As a further example, Applied E3 R2R “optimiz[es] process parameters from one run to the next,” as shown below:
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`Case 4:20-cv-00991-ALM Document 1-16 Filed 12/31/20 Page 12 of 12 PageID #: 336
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`PROCESS PARAMETER OPTIMIZATION
`
`E3 R2R improves
`processing performance
`and reduces process
`variability by optimizing
`process parameters from
`one run to the next. This
`
`example control chart
`shows a drifting process
`in furnace thickness
`
`uniformity that was
`corrected after making an
`adjustment.
`
`
`
`(Mean)
`
`MonitorThickness
`
`’USL - Upper Specification Limit
`
`‘LSL - Lower Specification Limit
`
`
`
`
`See id.
`See id.
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