Machine Learning based Optical Proximity Correction Techniques
The shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community. The optical proximity correction (OPC) is invented to reduce the errors of the lithographic process. The conventional OPC techniques rely on the empirical models and o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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JommPublish
2020-12-01
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| Series: | Journal of Microelectronic Manufacturing |
| Subjects: | |
| Online Access: | http://www.jommpublish.org/p/67/ |
| _version_ | 1818330749413097472 |
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| author | Pengpeng Yuan Taian Fan Yaobin Feng Peng Xu Yayi Wei |
| author_facet | Pengpeng Yuan Taian Fan Yaobin Feng Peng Xu Yayi Wei |
| author_sort | Pengpeng Yuan |
| collection | DOAJ |
| description | The shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community. The optical proximity correction (OPC) is invented to reduce the errors of the lithographic process. The conventional OPC techniques rely on the empirical models and optimization methods of iterative type. Both the accuracy and computing speed of the existing OPC techniques need to be improved to fulfill the stringent requirement of the research and design for latest technological nodes. The emergence of machine learning technologies inspires novel OPC algorithms. More accurate forward simulation of the lithographic process and single turn optimization methods are enabled by the machine learning based OPC techniques. We discuss the latest progress made by the OPC community in the process simulation and optimization based on machine learning techniques. |
| first_indexed | 2024-12-13T13:08:54Z |
| format | Article |
| id | doaj.art-bb3991776e8e408a98ad62f1b19edb2b |
| institution | Directory Open Access Journal |
| issn | 2578-3769 |
| language | English |
| last_indexed | 2024-12-13T13:08:54Z |
| publishDate | 2020-12-01 |
| publisher | JommPublish |
| record_format | Article |
| series | Journal of Microelectronic Manufacturing |
| spelling | doaj.art-bb3991776e8e408a98ad62f1b19edb2b2022-12-21T23:44:44ZengJommPublishJournal of Microelectronic Manufacturing2578-37692020-12-013410.33079/jomm.20030408Machine Learning based Optical Proximity Correction TechniquesPengpeng Yuan0Taian Fan1Yaobin Feng2Peng Xu 3Yayi Wei4Institute of Microelectronics, Chinese Academy of Science, Beijing 100029, ChinaInstitute of Microelectronics, Chinese Academy of Science, Beijing 100029, ChinaYangtze Memory Technologies Co, ., Ltd, Wuhan, Hubei 430000, ChinaInstitute of Microelectronics, Chinese Academy of Science, Beijing 100029, ChinaInstitute of Microelectronics, Chinese Academy of Science, Beijing 100029, China University of Chinese Academy of Science, Beijing 100029, ChinaThe shrinking of the size of the advanced technological nodes brings up new challenges to the semiconductor manufacturing community. The optical proximity correction (OPC) is invented to reduce the errors of the lithographic process. The conventional OPC techniques rely on the empirical models and optimization methods of iterative type. Both the accuracy and computing speed of the existing OPC techniques need to be improved to fulfill the stringent requirement of the research and design for latest technological nodes. The emergence of machine learning technologies inspires novel OPC algorithms. More accurate forward simulation of the lithographic process and single turn optimization methods are enabled by the machine learning based OPC techniques. We discuss the latest progress made by the OPC community in the process simulation and optimization based on machine learning techniques.http://www.jommpublish.org/p/67/optical proximity correctionmachine learningdeep learning; lithography |
| spellingShingle | Pengpeng Yuan Taian Fan Yaobin Feng Peng Xu Yayi Wei Machine Learning based Optical Proximity Correction Techniques Journal of Microelectronic Manufacturing optical proximity correction machine learning deep learning; lithography |
| title | Machine Learning based Optical Proximity Correction Techniques |
| title_full | Machine Learning based Optical Proximity Correction Techniques |
| title_fullStr | Machine Learning based Optical Proximity Correction Techniques |
| title_full_unstemmed | Machine Learning based Optical Proximity Correction Techniques |
| title_short | Machine Learning based Optical Proximity Correction Techniques |
| title_sort | machine learning based optical proximity correction techniques |
| topic | optical proximity correction machine learning deep learning; lithography |
| url | http://www.jommpublish.org/p/67/ |
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