AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns
Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical optimization and fast machine learning are the research focus of OPC in both academia and industry, each of which complements the other in terms of r...
Uloženo v:
| Vydáno v: | 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD) s. 1 - 9 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
ACM
29.10.2022
|
| Témata: | |
| ISSN: | 1558-2434 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical optimization and fast machine learning are the research focus of OPC in both academia and industry, each of which complements the other in terms of robustness or efficiency. We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity. Besides, we also find that many patterns repetitively appear in the design layout, and these patterns may possibly share optimized masks. We exploit these properties and propose a self-adaptive OPC framework to improve efficiency. Firstly we choose different OPC solvers adaptively for patterns of different complexity from an extensible solver pool to reach a speed/accuracy co-optimization. Apart from that, we prove the feasibility of reusing optimized masks for repeated patterns and hence, build a graph-based dynamic pattern library reusing stored masks to further speed up the OPC flow. Experimental results show that our framework achieves substantial improvement in both performance and efficiency. |
|---|---|
| AbstractList | Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical optimization and fast machine learning are the research focus of OPC in both academia and industry, each of which complements the other in terms of robustness or efficiency. We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity. Besides, we also find that many patterns repetitively appear in the design layout, and these patterns may possibly share optimized masks. We exploit these properties and propose a self-adaptive OPC framework to improve efficiency. Firstly we choose different OPC solvers adaptively for patterns of different complexity from an extensible solver pool to reach a speed/accuracy co-optimization. Apart from that, we prove the feasibility of reusing optimized masks for repeated patterns and hence, build a graph-based dynamic pattern library reusing stored masks to further speed up the OPC flow. Experimental results show that our framework achieves substantial improvement in both performance and efficiency. |
| Author | Chen, Guojin Ma, Yuzhe Yao, Xufeng Zhao, Wenqian Yu, Bei Yu, Ziyang Wong, Martin D.F. |
| Author_xml | – sequence: 1 givenname: Wenqian surname: Zhao fullname: Zhao, Wenqian organization: CUHK – sequence: 2 givenname: Xufeng surname: Yao fullname: Yao, Xufeng organization: CUHK – sequence: 3 givenname: Ziyang surname: Yu fullname: Yu, Ziyang organization: CUHK – sequence: 4 givenname: Guojin surname: Chen fullname: Chen, Guojin organization: CUHK – sequence: 5 givenname: Yuzhe surname: Ma fullname: Ma, Yuzhe organization: HKUST(GZ) – sequence: 6 givenname: Bei surname: Yu fullname: Yu, Bei organization: CUHK – sequence: 7 givenname: Martin D.F. surname: Wong fullname: Wong, Martin D.F. organization: CUHK |
| BookMark | eNotjEtLw0AUhUdRsNau3biYP5B6Z-bOI-5KNCpEUnysy23nRmLbpCRB0V9vQFfnfOeDcy5OmrZhIS4VzJVCe20sBGP13FhM0YUjMUt9GAWYVCuPx2KirA2JRoNnYtb3HwCgg1few0QUi0jlMruRC_nCuyoZ8TDUnyyfqN_Kcuz7-oeGum1k3tGev9puK_O2k89MO3nLff3eyCUNA3dNfyFOK9r1PPvPqXjL716zh6Qo7x-zRZGQxjAkTOvKVHaduogxYExjMKC9AwUBybtNqJjJw6aiDShnMMSIa0ugmVlHZ6bi6u-3HofVoav31H2vFIBLA2rzC0hpTw8 |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1145/3508352.3549468 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Xplore IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9781450392174 1450392172 |
| EISSN | 1558-2434 |
| EndPage | 9 |
| ExternalDocumentID | 10069842 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IH 6IL 6IN AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO FEDTE IEGSK IJVOP M43 OCL RIE RIL RIO |
| ID | FETCH-LOGICAL-a248t-eabf3f5b96d4d84d9d83027601084a76c8feea70cfac016348dd4b5a02eee2d63 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000981574300122&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:46:16 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a248t-eabf3f5b96d4d84d9d83027601084a76c8feea70cfac016348dd4b5a02eee2d63 |
| PageCount | 9 |
| ParticipantIDs | ieee_primary_10069842 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-Oct.-29 |
| PublicationDateYYYYMMDD | 2022-10-29 |
| PublicationDate_xml | – month: 10 year: 2022 text: 2022-Oct.-29 day: 29 |
| PublicationDecade | 2020 |
| PublicationTitle | 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD) |
| PublicationTitleAbbrev | ICCAD |
| PublicationYear | 2022 |
| Publisher | ACM |
| Publisher_xml | – name: ACM |
| SSID | ssj0002871770 ssj0020286 |
| Score | 2.2993999 |
| Snippet | Optical proximity correction (OPC) is a widely-used resolution enhancement technique (RET) for printability optimization. Recently, rigorous numerical... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Industries Layout Libraries Machine learning Robustness Search problems Shape |
| Title | AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns |
| URI | https://ieeexplore.ieee.org/document/10069842 |
| WOSCitedRecordID | wos000981574300122&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELagYoCFVxFveWBNcZ1LbLNVhYgB2oiH1K3yU0LQtOqD34_tpqULA5vjybnYd985932H0I1OlQPuM1XXzngCFHQi8xQSTXywZFyZzEFsNsF6PT4YiLImq0cujLU2Fp_ZVhjGf_lmrBfhqsyfcJILDt7jbjPGlmSt9YVKgP4sbL462_ITea3l04bsNs0i2GilPiGCIKy60UwlxpJi_5-rOEDNX1YeLtfx5hBt2eoI7W0ICh6jp46R_bJ7hzv41X65xD9Ogj_Dz3L2ift-PKp5l7hYVWXhYjzFLx4v4vtYzYHLKLlZzZrovXh46z4mdb-ERFLg88RK5VKXKZEbMByMMEHcKxS9EA6S5Zo7ayUj2kntkV4K3BhQmSTUvyM1eXqCGtW4sqcIa0Ok8GBKEUUhc0wyR6gmxsncWSXMGWoGwwwnS0mM4com53_MX6BdGngD3ulTcYka8-nCXqEd_T3_mE2v44f8AUyqnWo |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV25TgMxELVQQAIariBuXNBucLzjXS9dFFgFkWMFQUoX-ZQQkEQ5-H5sZwlpKOhsV77njT3vDUI3KpYWuPNUbZ3xCCioSCQxRIo4Y5lyqZmFkGwi7Xb5YJAVJVk9cGGMMSH4zNR8Mfzl67Fa-Kcyd8JJknFwN-4mA6D1JV1r9aTiwX_qt1_pb7mGpFTzqQO7jVmAG7XYuUTgpVXX0qkEa5Lv_bMf-6j6y8vDxcriHKANMzpEu2uSgkeo3dCiVzTvcAO_mA8buerE32i4I2bvuOfKnyXzEuc_cVk4H0_xs0OM-D7Ec-AiiG6OZlX0mj_0m62ozJgQCQp8HhkhbWyZzBINmoPOtJf38mEvhINIE8WtMSIlygrlsF4MXGuQTBDqxkh1Eh-jymg8MicIK01E5uCUJJICs6lILaGKaCsSa2SmT1HVT8xwshTFGP7Mydkf7ddou9XvtIftx-7TOdqhnkXgTADNLlBlPl2YS7SlvuZvs-lVWNRvrcugsQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2022+IEEE%2FACM+International+Conference+On+Computer+Aided+Design+%28ICCAD%29&rft.atitle=AdaOPC%3A+A+Self-Adaptive+Mask+Optimization+Framework+For+Real+Design+Patterns&rft.au=Zhao%2C+Wenqian&rft.au=Yao%2C+Xufeng&rft.au=Yu%2C+Ziyang&rft.au=Chen%2C+Guojin&rft.date=2022-10-29&rft.pub=ACM&rft.eissn=1558-2434&rft.spage=1&rft.epage=9&rft_id=info:doi/10.1145%2F3508352.3549468&rft.externalDocID=10069842 |