IP Library Granted Patent US 12,710,694
Granted Patent B2
US 12,710,694 · App. 18/814,426 · Granted Aug 18, 2026

Mask optimization for first layer that accounts for other layers

Inventors: Donald Oriordan (Sunnyvale, CA); Akira Fujimura (Saratoga, CA)
Assignee: D2S, INC.
G03F1/70G03F1/36G03F1/72G03F7/70441G03F7/705G06F30/398G03F7/70633G06F30/367G06F2119/18G06F2119/22
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,710,694
App. No.
18/814,426
Filed
Aug 23, 2024
Granted
Aug 18, 2026
Kind
B2
Examiner
TAT, BINH C
Art Unit
2851
USPC
716/53
Abstract

Some embodiments provide a method for optimizing a mask layout for producing masks that are used for manufacturing an integrated circuit (IC) comprising multiple layers of components. The method receives a mask layout including a set of mask images corresponding to a first layer of components of the IC that is adjacent to at least a second layer of components. The method generates a first wafer image including representations of IC components that are predicted to be manufactured for the first layer based on the received set of mask images corresponding to the first layer. Based on a positional relationship between at least one predicted IC component in the first wafer image and at least one predicted IC component in a second wafer image for the second layer, the method modifies at least one mask image in the set of mask images for the first layer.

Claims (42)

1 . A method for optimizing a mask layout for producing masks that are used for manufacturing an integrated circuit (IC) comprising multiple layers of components, the method comprising:

receiving a mask layout comprising a set of mask images corresponding to a first layer of components of the IC that is adjacent to at least a second layer of components;

generating a second-layer wafer image for the second layer of components based on a set of mask images corresponding to the second layer; and

iteratively:

generating a first-layer wafer image comprising representations of IC components that are predicted to be manufactured for the first layer based on a current set of mask images corresponding to the first layer;

and based on a positional relationship between at least one predicted IC component in the first-layer wafer image and at least one predicted IC component in the second-layer wafer image, modifying at least one mask image in the current set of mask images for the first layer to generate a modified set of mask images for the first layer,

wherein the second-layer wafer image is generated once and used each iteration for modifying the at least one mask image for the first layer.

2 . The method of claim 1 , wherein the set of mask images, when optimized, is used to fabricate a set of masks used for manufacturing the first layer of the IC.

3 . The method of claim 1 , wherein modifying the at least one mask image comprises:

identifying an objective function that accounts for (i) a difference between the first-layer wafer image and a target wafer image for the first layer and (ii) interaction of predicted IC components in the first-layer wafer image with predicted IC components in the second-layer wafer image; and

modifying the at least one mask image based on a calculated value for the objective function.

4 . The method of claim 1 , wherein:

the first layer is a metal layer and the second layer is a via layer;

the predicted IC component in the first-layer wafer image is a representation of an interconnect wire segment and the predicted IC component in the second-layer wafer image is a representation of a via that connects to the interconnect wire segment.

5 . The method of claim 1 , wherein:

the first layer is a via layer and the second layer is a metal layer;

the predicted IC component in the first-layer wafer image is a representation of a via and the predicted IC component in the second-layer wafer image is a representation of an interconnect wire segment that connects to the via.

6 . The method of claim 1 , wherein generating the first-layer wafer image comprises simulating a set of lithographic processes used to fabricate the first layer of the IC using a set of masks based on the set of mask images.

7 . The method of claim 1 , wherein generating the first-layer wafer image comprises providing the set of mask images as input to a machine-trained network that outputs the first-layer wafer image.

8 . The method of claim 1 , wherein generating the first-layer wafer image comprises:

rasterizing the set of mask images into a set of mask pixel images; and

generating the first-layer wafer image as a pixel image from the set of mask pixel images.

9 . The method of claim 1 , wherein modifying the at least one mask image comprises modifying a mask shape in one of the mask images that is used to produce the IC component in order to modify a shape of the produced IC component.

10 . A non-transitory machine-readable medium storing a program which when executed by at least one processing unit optimizes a mask layout for producing masks that are used for manufacturing an integrated circuit (IC) comprising multiple layers of components, the program comprising sets of instructions for:

receiving a mask layout comprising a set of mask images corresponding to a first layer of components of the IC that is adjacent to at least a second layer of components;

generating a second-layer wafer image for the second layer of components based on a set of mask images corresponding to the second layer; and

iteratively:

generating a first-layer wafer image comprising representations of IC components that are predicted to be manufactured for the first layer based on a current set of mask images corresponding to the first layer; and

based on a positional relationship between at least one predicted IC component in the first-layer wafer image and at least one predicted IC component in the second-layer wafer image, modifying at least one mask image in the current set of mask images for the first layer to generate a modified set of mask images for the first layer,

wherein the second-layer wafer image is generated once and used each iteration for modifying the at least one mask image for the first layer.

11 . The non-transitory machine-readable medium of claim 10 , wherein the set of mask images, when optimized, is used to fabricate a set of masks used for manufacturing the first layer of the IC.

12 . The non-transitory machine-readable medium of claim 10 , wherein the set of instructions for modifying the at least one mask image comprises sets of instructions for:

identifying an objective function that accounts for (i) a difference between the first-layer wafer image and a target wafer image for the first layer and (ii) interaction of predicted IC components in the first-layer wafer image with predicted IC components in the second-wafer wafer image; and

modifying the at least one mask image based on a calculated value for the objective function.

13 . The non-transitory machine-readable medium of claim 10 , wherein:

the first layer is a metal layer and the second layer is a via layer; and

the predicted IC component in the first-layer wafer image is a representation of an interconnect wire segment and the predicted IC component in the second-wafer wafer image is a representation of a via that connects to the interconnect wire segment.

14 . The non-transitory machine-readable medium of claim 10 , wherein:

the first layer is a via layer and the second layer is a metal layer; and

the predicted IC component in the first-layer wafer image is a representation of a via and the predicted IC component in the second-layer wafer image is a representation of an interconnect wire segment that connects to the via.

15 . The non-transitory machine-readable medium of claim 10 , wherein the set of instructions for generating the first-layer wafer image comprises a set of instructions for simulating a set of lithographic processes used to fabricate the first layer of the IC using a set of masks based on the set of mask images.

16 . The non-transitory machine-readable medium of claim 10 , wherein the set of instructions for generating the first-layer wafer image comprises a set of instructions for providing the set of mask images as input to a machine-trained network that outputs the first-layer wafer image.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 12, 2024
From: ORIORDAN, DONALD; FUJIMURA, AKIRA
To: D2S, INC.
Reel/Frame 069240/0194 →
Continuity (4)
Provisional Application 63679793 · Aug 6, 2024
Provisional Application 63608141 · Dec 8, 2023
Provisional Application 63534137 · Aug 23, 2023
Related Publication 20250068051A1 · Feb 27, 2025
References Cited (247)
US 5135609A · Pease et al. · 1992 [cited by applicant]
US 5326659A · Liu et al. · 1994 [cited by applicant]
US 5527645A · Pati et al. · 1996 [cited by applicant]
US 5553274A · Liebmann · 1996 [cited by applicant]
US 5682323A · Pasch et al. · 1997 [cited by applicant]
US 5698346A · Sugawara · 1997 [cited by applicant]
US 5723233A · Garza et al. · 1998 [cited by applicant]
US 5801954A · Le et al. · 1998 [cited by applicant]
US 5827623A · Ishida et al. · 1998 [cited by applicant]
US 5849440A · Lucas et al. · 1998 [cited by applicant]
US 5879844A · Yamamoto et al. · 1999 [cited by applicant]
US 6187483B1 · Capodieci et al. · 2001 [cited by applicant]
US 6269472B1 · Garza et al. · 2001 [cited by applicant]
US 6467076B1 · Cobb · 2002 [cited by applicant]
US 6510730B1 · Phan et al. · 2003 [cited by applicant]
US 6541167B2 · Petersen et al. · 2003 [cited by applicant]
US 6611953B1 · Filseth et al. · 2003 [cited by applicant]
US 6803554B2 · Ye et al. · 2004 [cited by applicant]
US 6807503B2 · Ye et al. · 2004 [cited by applicant]
US 6892365B2 · Culp et al. · 2005 [cited by applicant]
US 6978438B1 · Capodieci · 2005 [cited by applicant]
US 6996797B1 · Liebmann et al. · 2006 [cited by applicant]
US 7003758B2 · Ye et al. · 2006 [cited by applicant]
US 7063920B2 · Baba-Ali · 2006 [cited by applicant]
US 7080349B1 · Babcock et al. · 2006 [cited by applicant]
US 7266803B2 · Chou et al. · 2007 [cited by applicant]
US 7269817B2 · Heng et al. · 2007 [cited by applicant]
US 7353493B2 · Akiyama · 2008 [cited by applicant]
US 7493590B1 · Hess et al. · 2009 [cited by applicant]
US 7509620B2 · Davids · 2009 [cited by applicant]
US 7526748B2 · Kotani et al. · 2009 [cited by applicant]
US 7545984B1 · Kiel et al. · 2009 [cited by applicant]
US 7546574B2 · Torunoglu et al. · 2009 [cited by applicant]
US 7589819B2 · Baba-Ali · 2009 [cited by applicant]
US 7694267B1 · Ye et al. · 2010 [cited by applicant]
US 7703069B1 · Liu et al. · 2010 [cited by applicant]
US 7716627B1 · Jeffrey et al. · 2010 [cited by applicant]
US 7754401B2 · Fujimura et al. · 2010 [cited by applicant]
US 7856612B1 · Ungar et al. · 2010 [cited by applicant]
US 7921383B1 · Wei · 2011 [cited by applicant]
US 8166423B2 · Mansfield et al. · 2012 [cited by applicant]
US 8238644B2 · Brunner et al. · 2012 [cited by applicant]
US 8336006B2 · Kodera et al. · 2012 [cited by applicant]
US 8429573B2 · Ogino et al. · 2013 [cited by applicant]
US 8473875B2 · Fujimura et al. · 2013 [cited by applicant]
US 8478808B1 · Torunoglu · 2013 [cited by applicant]
US 8490034B1 · Torunoglu et al. · 2013 [cited by applicant]
US 8512919B2 · Fujimura et al. · 2013 [cited by applicant]
US 8719739B2 · Fujimura et al. · 2014 [cited by applicant]
US 8818072B2 · Ong et al. · 2014 [cited by applicant]
US 8839169B2 · Gyoda et al. · 2014 [cited by applicant]
US 9257367B2 · Okada et al. · 2016 [cited by applicant]
US 9424372B1 · Torunoglu et al. · 2016 [cited by applicant]
US 9672320B2 · Chang et al. · 2017 [cited by applicant]
US 9922161B2 · Kahng et al. · 2018 [cited by applicant]
US 10146124B2 · Li et al. · 2018 [cited by applicant]
US 10310371B2 · Ye et al. · 2019 [cited by applicant]
US 10444629B2 · Zable · 2019 [cited by applicant]
US 10520830B2 · Kicken et al. · 2019 [cited by applicant]
US 10534257B2 · Tetiker et al. · 2020 [cited by applicant]
US 10670973B2 · Zou et al. · 2020 [cited by applicant]
US 10678142B2 · Jheng et al. · 2020 [cited by applicant]
US 11243473B2 · Wang et al. · 2022 [cited by applicant]
US 11644746B1 · Xiao et al. · 2023 [cited by applicant]
US 20020091986A1 · Ferguson et al. · 2002 [cited by applicant]
US 20030005390A1 · Takashima et al. · 2003 [cited by applicant]
US 20030044692A1 · Liu et al. · 2003 [cited by applicant]
US 20040015794A1 · Kotani et al. · 2004 [cited by applicant]
US 20040057610A1 · Filseth et al. · 2004 [cited by applicant]
US 20040088149A1 · Cobb · 2004 [cited by applicant]
US 20040210863A1 · Culp et al. · 2004 [cited by applicant]
US 20050076321A1 · Smith · 2005 [cited by applicant]
US 20050100802A1 · Callan et al. · 2005 [cited by applicant]
US 20050132306A1 · Smith et al. · 2005 [cited by applicant]
US 20050138594A1 · Sahouria et al. · 2005 [cited by applicant]
US 20050138597A1 · Aleshin et al. · 2005 [cited by applicant]
US 20050149902A1 · Shi et al. · 2005 [cited by applicant]
US 20050251771A1 · Robles · 2005 [cited by applicant]
US 20060073686A1 · Zach et al. · 2006 [cited by applicant]
US 20060190911A1 · Stivers · 2006 [cited by applicant]
US 20060242618A1 · Wang et al. · 2006 [cited by applicant]
US 20060248495A1 · Sezginer · 2006 [cited by applicant]
US 20070011648A1 · Abrams · 2007 [cited by applicant]
US 20070031745A1 · Ye et al. · 2007 [cited by applicant]
US 20070032896A1 · Ye et al. · 2007 [cited by applicant]
US 20070037394A1 · Su et al. · 2007 [cited by applicant]
US 20070050749A1 · Ye et al. · 2007 [cited by applicant]
US 20070124708A1 · Torres Robles et al. · 2007 [cited by applicant]
US 20070130557A1 · Luk-Pat et al. · 2007 [cited by applicant]
US 20070130559A1 · Torunoglu et al. · 2007 [cited by applicant]
US 20070143234A1 · Huang et al. · 2007 [cited by applicant]
US 20070184357A1 · Abrams et al. · 2007 [cited by applicant]
US 20070186206A1 · Abrams et al. · 2007 [cited by applicant]
US 20070198963A1 · Granik et al. · 2007 [cited by applicant]
US 20070220476A1 · Mukherjee et al. · 2007 [cited by applicant]
US 20070230770A1 · Kulkarni et al. · 2007 [cited by applicant]
US 20080077907A1 · Kulkami · 2008 [cited by applicant]
US 20080109766A1 · Song et al. · 2008 [cited by applicant]
US 20080141211A1 · Bruce et al. · 2008 [cited by applicant]
US 20090037852A1 · Kobayashi et al. · 2009 [cited by applicant]
US 20090075183A1 · Cecil · 2009 [cited by applicant]
US 20090077527A1 · Gergov et al. · 2009 [cited by applicant]
US 20090148779A1 · Baidya et al. · 2009 [cited by applicant]
US 20090193369A1 · Chan et al. · 2009 [cited by applicant]
US 20090241077A1 · Lippincott et al. · 2009 [cited by applicant]
US 20090245618A1 · Torunoglu et al. · 2009 [cited by applicant]
US 20090307649A1 · Pramanik et al. · 2009 [cited by applicant]
US 20100153903A1 · Inoue et al. · 2010 [cited by applicant]
US 20100280812A1 · Zhang · 2010 [cited by applicant]
US 20110004856A1 · Granik et al. · 2011 [cited by applicant]
US 20110022994A1 · Hu et al. · 2011 [cited by applicant]
US 20110049635A1 · Carlson · 2011 [cited by applicant]
US 20110061030A1 · Mansfield et al. · 2011 [cited by applicant]
US 20110089345A1 · Komagata et al. · 2011 [cited by applicant]
US 20110219342A1 · Socha · 2011 [cited by examiner]
US 20110239169A1 · Tirapu-Azpiroz et al. · 2011 [cited by applicant]
US 20120017183A1 · Ye et al. · 2012 [cited by applicant]
US 20120066651A1 · Pang et al. · 2012 [cited by applicant]
US 20120094219A1 · Fujimura et al. · 2012 [cited by applicant]
US 20120151422A1 · White et al. · 2012 [cited by applicant]
US 20130152026A1 · Peng et al. · 2013 [cited by applicant]
US 20130159943A1 · Agarwal et al. · 2013 [cited by applicant]
US 20130283216A1 · Pearman et al. · 2013 [cited by applicant]
US 20130283218A1 · Fujimura et al. · 2013 [cited by applicant]
US 20140123082A1 · Akhssay · 2014 [cited by examiner]
US 20140129996A1 · Fujimura et al. · 2014 [cited by applicant]
US 20140272676A1 · Satake et al. · 2014 [cited by applicant]
US 20140282290A1 · Rieger et al. · 2014 [cited by applicant]
US 20150169820A1 · Wang · 2015 [cited by applicant]
US 20150302134A1 · Berkens · 2015 [cited by applicant]
US 20160026750A1 · Liu · 2016 [cited by applicant]
US 20160042111A1 · Chang · 2016 [cited by applicant]
US 20160162621A1 · Hamouda · 2016 [cited by applicant]
US 20160239601A1 · Morisaki et al. · 2016 [cited by applicant]
US 20160253450A1 · Kandel et al. · 2016 [cited by applicant]
US 20160283631A1 · Lin et al. · 2016 [cited by applicant]
US 20170061044A1 · Ning et al. · 2017 [cited by applicant]
US 20170357911A1 · Liu et al. · 2017 [cited by applicant]
US 20180120709A1 · Hsu et al. · 2018 [cited by applicant]
US 20180247008A1 · Hamouda · 2018 [cited by applicant]
US 20190072846A1 · Lin et al. · 2019 [cited by applicant]
US 20190094680A1 · Huang et al. · 2019 [cited by applicant]
US 20190094710A1 · Wang · 2019 [cited by examiner]
US 20190137889A1 · Tel et al. · 2019 [cited by applicant]
US 20190146455A1 · Beylkin et al. · 2019 [cited by applicant]
US 20190147134A1 · Wang et al. · 2019 [cited by applicant]
US 20190188356A1 · Zhang et al. · 2019 [cited by applicant]
US 20190197213A1 · Ungar · 2019 [cited by applicant]
US 20190206041A1 · Fang et al. · 2019 [cited by applicant]
US 20190346768A1 · Huang et al. · 2019 [cited by applicant]
US 20190354006A1 · Hu et al. · 2019 [cited by applicant]
US 20190392106A1 · Northrop et al. · 2019 [cited by applicant]
US 20200006102A1 · Lin et al. · 2020 [cited by applicant]
US 20200051781A1 · Fujimura et al. · 2020 [cited by applicant]
US 20200065453A1 · Kim et al. · 2020 [cited by applicant]
US 20200133117A1 · Chu et al. · 2020 [cited by applicant]
US 20200184137A1 · Tsutsui et al. · 2020 [cited by applicant]
US 20200326632A1 · Fang et al. · 2020 [cited by applicant]
US 20200380362A1 · Cao et al. · 2020 [cited by applicant]
US 20210048741A1 · Lugg et al. · 2021 [cited by applicant]
US 20210048753A1 · Zhang et al. · 2021 [cited by applicant]
US 20210072635A1 · Ma et al. · 2021 [cited by applicant]
US 20210141988A1 · Ungar et al. · 2021 [cited by applicant]
US 20210149312A1 · Tel et al. · 2021 [cited by applicant]
US 20210181620A1 · Poonawala et al. · 2021 [cited by applicant]
US 20210216697A1 · Brink et al. · 2021 [cited by applicant]
US 20210232748A1 · Hsu et al. · 2021 [cited by applicant]
US 20210357571A1 · Tien et al. · 2021 [cited by applicant]
US 20210397172A1 · Slachter et al. · 2021 [cited by applicant]
US 20220035237A1 · Lee et al. · 2022 [cited by applicant]
US 20220050381A1 · Biswas et al. · 2022 [cited by applicant]
US 20220128899A1 · Fujimura et al. · 2022 [cited by applicant]
US 20220138381A1 · Yamane et al. · 2022 [cited by applicant]
US 20220187713A1 · Middlebrooks et al. · 2022 [cited by applicant]
US 20230092665A1 · Fujimura et al. · 2023 [cited by applicant]
US 20230107556A1 · Tao et al. · 2023 [cited by applicant]
US 20230168590A1 · Sin et al. · 2023 [cited by applicant]
US 20230168660A1 · Oriordan et al. · 2023 [cited by applicant]
US 20230229840A1 · Oriordan et al. · 2023 [cited by applicant]
US 20230280646A1 · Kim et al. · 2023 [cited by applicant]
US 20230280659A1 · Fu · 2023 [cited by applicant]
US 20230289509A1 · Kunigal et al. · 2023 [cited by applicant]
US 20240220702A1 · Thulasi et al. · 2024 [cited by applicant]
US 20240288764A1 · Hamouda · 2024 [cited by applicant]
US 20240334611A1 · Thulasi et al. · 2024 [cited by applicant]
US 20250068052A1 · Oriordan et al. · 2025 [cited by applicant]
US 20250068053A1 · Oriordan et al. · 2025 [cited by applicant]
US 20250068056A1 · Oriordan et al. · 2025 [cited by applicant]
US 20250068057A1 · Oriordan et al. · 2025 [cited by applicant]
US 20250068058A1 · Oriordan et al. · 2025 [cited by applicant]
US 20250102899A1 · Oriordan et al. · 2025 [cited by applicant]
US 20250348641A1 · Hamouda · 2025 [cited by applicant]
EP 3951496A1 · 2022 [cited by applicant]
JP 2009004699A · 2009 [cited by applicant]
JP 2012181298A · 2012 [cited by applicant]
JP 2019114295A · 2019 [cited by applicant]
WO 2018125220A1 · 2018 [cited by applicant]
WO 2020156777A1 · 2020 [cited by applicant]
WO 2020193095A1 · 2020 [cited by applicant]
WO 2021041963A1 · 2021 [cited by applicant]
WO 2021043936A1 · 2021 [cited by applicant]
WO 2025043234A2 · 2025 [cited by applicant]
Pang, Linyong, et al., “TrueMask ILT MWCO: Full-Chip Curvilinear ILT in a Day, and Full Mask Multi-Beam and VSB Writing in 12 Hours for 193i”, Optical Microlithography XXXIII, Proc. of SPIE vol. 11327, Mar. 2020, 14 pag… [cited by applicant]
Author Unknown, “Boolean operations on polygons,” Wikipedia, Oct. 12, 2019, 3 pages, Wikipedia.com. [cited by applicant]
Author Unknown, “D2S Enables “Stitchless” Full-Chip Inverse Lithography Technology in a Single Day for the Multi-Beam Era,” Press Release, Sep. 16, 2019, 3 pages, D2S, Inc., San Jose, California, USA. [cited by applicant]
Author Unknown, “D2S Unveils Industry's First Mask-Wafer Double Simulation Platform,” Press Release, Sep. 20, 2011, 3 pages, D2S, Inc., San Jose, California, USA. [cited by applicant]
Author Unknown, “Jaccard index,” Wikipedia, Sep. 6, 2020, 9 pages, Wikipedia.com. [cited by applicant]
Author Unknown, “Optical proximity correction,” Wikipedia, Apr. 28, 2019, 5 pages, Wikipedia.com. [cited by applicant]
Author Unknown, “Rasterisation,” Wikipedia, Aug. 21, 2020, 4 pages, Wikipedia.com. [cited by applicant]
Author Unknown, “TrueMask® DS,” Exact Date Unknown but Before May 2020, 3 pages, D2S, Inc., retrieved from https://design2silicon.com/products/truemask-ds/. [cited by applicant]
Author Unknown, “TrueMask® ILT Backgrounder Stitchless Full-Chip ILT in a Day,” Backgrounder, Sep. 2019, 6 pages, D2S, Inc., San Jose, California, USA. [cited by applicant]
Author Unknown, “TrueMask® ILT,” Exact Date Unknown but Before May 2020, 7 pages, D2S, Inc., retrieved from https://design2silicon.com/products/truemask-ilt/. [cited by applicant]
Author Unknown, “Design for Manufacturing (DFM),” Mar. 22, 2023, 5 pages, Semiconductor Engineering, retrieved from https://web.archive.org/web/20230322153514/https://semiengineering.com/knowledge_centers/eda-design/met… [cited by applicant]
Cecil, Thomas, et al., “Establishing Fast, Practical, Full-Chip ILT Flows Using Machine Learning,” SPIE Proceedings 11327, Optical Microlithography XXXIII, Mar. 23, 2020, 19 pages, vol. 1132706, SPIE, San Jose, Californ… [cited by applicant]
Chen, Kun-Yuan, et al., “Full-Chip Application of Machine Learning SRAFs on DRAM Case Using Auto Pattern Selection,” SPIE Proceedings 10961, Optical Microlithography XXXII, Oct. 10, 2019, 13 pages, vol. 1096108, SPIE, S… [cited by applicant]
Chen, Tai-Chen, “Multilevel Full-Chip Gridless Routing With Applications to Optical-Proximity Correction,” Jun. 2007, 13 pages, IEEE, retrieved from http://cc.ee.ntu.edu.tw/~ywchang/Papers/tcad07-mgr.pdf. [cited by applicant]
Dillinger, Tom, “Inverse Lithography Technology—A Status Update from TSMC,” SemiWiki, Jun. 2, 2022, retrieved from https://semiwiki.com/semiconductor-manufacturers/tsmc/313540-inverse-lithography-technology-a-status-upd… [cited by applicant]
Feng, Yaobin, et al., “Freeform Mask Optimization Using Advanced Image based M3D Inverse Lithography and 3D-NAND Full Chip OPC Application,” SPIE Proceedings 10587, Optical Microlithography XXXI, Mar. 20, 2018, 11 pages… [cited by applicant]
Jia, Ningning, et al., “Machine Learning for Inverse Lithography: using stochastic gradient descent for robust photomask synthesis,” Journal of Optics, Apr. 1, 2010, 9 pages, vol. 12, IOP Publishing, retrieved from http… [cited by applicant]
Jiang, Bentian et al., “Neural-ILT: Migrating ILT to Neural Networks for Mask Printability and Complexity Cooptimization,” International Conference on Computer-Aided Design, Nov. 2020 (ICCAD '20), 9 pages, Association f… [cited by applicant]
Kwan, Joe, et al., “Applying Machine Learning Techniques to Accelerate Advanced Process Yield Ramp,” Jan. 5, 2021, 29 pages, Siemens Digital Industries Software, Munich, Germany. [cited by applicant]
Lee, Kuang-Yao, et al., “Post-Routing Redundant Via Insertion for Yield/Reliability Improvement,” ASP-DAC 2006: 11th Asia and South Pacific Design Automation Conference, Jan. 24-27, 2006, 6 pages, IEEE, Yokohama, Japan. [cited by applicant]
Lin, Yibo, et al., “Machine Learning for Yield Learning and Optimization,” 2018 IEEE International Test Conference (ITC), Oct. 29-Nov. 1, 2018, 10 pages, IEEE, Phoenix, AZ, USA. [cited by applicant]
Liu, Peng, “Mask Synthesis Using Machine Learning Software and Hardware Platforms,” SPIE Proceedings 11327, Optical Microlithography XXXIII, Mar. 23, 2020, 17 pages, vol. 1132707, SPIE, San Jose, California, USA. [cited by applicant]
Pang, Linyong (Leo), et al., “Study of Mask and Wafer Co-design That Utilizes a New Extreme SIMD Approach to Computing in Memory Manufacturing: Full-Chip Curvilinear ILT in a Day,” SPIE Proceedings 11148, Photomask Tech… [cited by applicant]
Pang, Linyong, “Inverse Lithography Technology: 30 years from concept to practical, full-chip reality,” Journal of Micro/Nanopatterning, Materials, and Metrology, Aug. 31, 2021, 49 pages, vol. 20(3), SPIE, retrieved fro… [cited by applicant]
Pang, Linyong, et al., “How GPU-Accelerated Simulation Enables Applied Deep Learning for Masks and Wafers,” Photomask Japan 2019: XXVI Symposium on Photomask and Next-Generation Lithography Mask Technology, Apr. 16-18, … [cited by applicant]
Pang, Linyong, et al., “Making Digital Twins using the Deep Learning Kit (DLK),” Photomask Technology 2019, Sep. 15-19, 2019, 13 pages, SPIE, Monterey, California, USA. [cited by applicant]
PCT International Search Report and Written Opinion of Commonly Owned International Patent Application PCT/US2024/043770, mailing date Nov. 13, 2024, 13 pages, International Searching Authority (US). [cited by applicant]
Pearman, Ryan, et. al., “Adopting curvilinear shapes for production ILT: challenges and opportunities,” Photomask Technology 2019, Oct. 10, 2019, 14 pages, SPIE, retrieved from https://design2silicon.com/wp-content/uplo… [cited by applicant]
Ren, Haoxing (Mark), “Machine Learning and Deep Learning Applications in Design Automation and Practical Issues,” DAC '19: 56th Annual Design Automation Conference 2019, Jun. 2-6, 2019, 55 pages, Association for Computi… [cited by applicant]
Shi, Xuelong, et al., “Physics based feature Vector Design: a Critical Step Towards Machine Learning based Inverse Lithography,” SPIE Proceedings 11327, Optical Microlithography XXXIII, Mar. 23, 2020, 8 pages, vol. 1132… [cited by applicant]
Sole, Marc Pons, “Layout Regularity for Design and Manufacturability,” Doctoral Thesis, Jul. 8, 2012, 185 pages, Technical University of Catalonia, Barcelona, Spain. [cited by applicant]
Sundareswaran, Savithri, et al., “A Sensitivity-aware Methodology to Improve Cell Layouts for DFM Guidelines,” 5 pgs., 12th Int'l Symposium on Quality Electronic Design, Mar. 14-16, 2011, Santa Clara, CA. [cited by applicant]
Teig, Steven L., “The X architecture: not your father's diagonal wiring,” SLIP '02: Proceedings of the 2002 international workshop on System-level interconnect prediction, Apr. 6, 2002, 5 pages, ACM, retrieved from http… [cited by applicant]
Tripathi, Vikas, et al., “Context-Aware DFM Rule Analysis and Scoring Using Machine Learning,” Aug. 16, 2018, 9 pages, retrieved from https://arxiv.org/pdf/1808.05999. [cited by applicant]
Tripathi, Vikas, et al., “In-design DFM rule scoring and fixing method using ICV,” Synopsys User Group Penang (SNUG), May 25, 2018, 16 pages, retrieved from https://arxiv.org/pdf/1805.10016. [cited by applicant]
Wang, Shibing, et al., “Efficient Full-Chip SRAF Placement Using Machine Learning for Best Accuracy and Improved Consistency,” SPIE Proceedings 10587, Optical Microlithography XXXI, Mar. 20, 2018, 10 pages, vol. 105870N… [cited by applicant]
Wang, Shibing, et al., “Machine Learning Assisted SRAF Placement for Full Chip,” SPIE Proceedings 10451, Photomask Technology 2017, Oct. 16, 2017, 8 pages, vol. 104510D, SPIE, Monterey, California, USA. [cited by applicant]
Ye, Wei, et al., “LithoGAN: End-to-End Lithography Modeling with Generative Adversarial Networks,” Design Automation Conference, Jun. 2019 (DAC '19), 6 pages, Association for Computing Machinery. [cited by applicant]
Dong, Xuan, et al., “Process-Variation-Aware Rule-Based Optical Proximity Correction for Analog Layout Migration,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, No. 8, Aug. 1, 2… [cited by applicant]
Elsemary, Ahmed Mounir, et al., “Litho Friendly Via insertion with In-Design Auto-Fix flow using Machine Learning” Proceedings of SPIE, vol. 10588, Design-Process-Technology Co-optimization for Manufacturability XII, Ap… [cited by applicant]
Kareem, Pervaiz, et al., “Fast Prediction of Process Variation Band through Machine Learning Models,” Optical Microlithography XXXIV, Feb. 2021, vol. 11613, 8 pages, Proceedings of SPIE—The International Society for Opt… [cited by applicant]
Liu, Yong, et al., “Inverse Lithography Technology Principles in Practice: Unintuitive Patterns” Proceedings SPIE 5992, 25th Annual BACUS Symposium on Photomask Technology, Nov. 8, 2005, 8 pages, SPIE. [cited by applicant]
Ma, Yuzhe, et al., “Methodologies for Layout Decomposition and Mask Optimization: A Systematic Review,” Proceedings of the IFIP/IEEE International Conference on Very Large Scale Integration, Oct. 23-25, 2017, United Ara… [cited by applicant]
Shao, Dongbing, et al., “Incorporating Process Variation Contours in Design Rule Calculation and SRAM Design Optimization”, Proceedings of SPIE, vol. 10962, Design-Process-Technology Co-optimization for Manufacturabilit… [cited by applicant]
Shao, Hao-Chiang, et al., “From IC Layout to Die Photograph: A CNN-Based Data-Driven Approach” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 40, No. 5, May 2021, pp. 957-970, IEEE. [cited by applicant]