{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:58:20Z","timestamp":1780675100065,"version":"3.54.1"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2019,12,19]],"date-time":"2019-12-19T00:00:00Z","timestamp":1576713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61872136"],"award-info":[{"award-number":["61872136"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2020,3,31]]},"abstract":"<jats:p>With the increasing gap between transistor feature size and lithography manufacturing capability, the detection of lithography hotspots becomes a key stage of physical verification flow to enhance manufacturing yield. Although machine learning approaches are distinguished for their high detection efficiency, they still suffer from problems such as large-scale layout and class imbalance. In this article, we develop a hotspot detection model based on machine learning with high performance. In the proposed model, we first apply an Fast Fourier Transform--based feature extraction method that can compress large-scale layout to a multi-dimensional representation with much smaller size while preserving the discriminative layout pattern information to improve the detection efficiency. Second, addressing the class imbalance problem, we propose a new technique called imbalanced learning rate and embed it into the convolutional neural network model to further reduce false alarms without accuracy decay. Compared with the results of current state-of-the-art approaches on ICCAD 2012 Contest benchmarks, our proposed model can achieve better solutions in many evaluation metrics, including the official metrics.<\/jats:p>","DOI":"10.1145\/3372044","type":"journal-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T13:33:12Z","timestamp":1576848792000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Lithography Hotspot Detection with FFT-based Feature Extraction and Imbalanced Learning Rate"],"prefix":"10.1145","volume":"25","author":[{"given":"Xu","family":"He","sequence":"first","affiliation":[{"name":"Hunan University, Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Deng","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shizhe","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Defence Technology, Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Guo","sequence":"additional","affiliation":[{"name":"National University of Defence Technology, Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,12,19]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"E. 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PLOS One 12 6 (2017).  S. Boughorbel F. Jarray and M. El-Anbari. 2017. Optimal classifier for imbalanced data using matthews correlation coefficient metric. PLOS One 12 6 (2017).","DOI":"10.1371\/journal.pone.0177678"},{"key":"e_1_2_1_4_1","unstructured":"Jake Bouvrie. 2006. Notes on convolutional neural networks. http:\/\/web.mit.edu\/jvb\/www\/papers\/cnn_tutorial.pdf.  Jake Bouvrie. 2006. Notes on convolutional neural networks. http:\/\/web.mit.edu\/jvb\/www\/papers\/cnn_tutorial.pdf."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMM.18.1.013507"},{"key":"e_1_2_1_6_1","volume-title":"Pan","author":"Ding Duo","year":"2011","unstructured":"Duo Ding , Andres J. Torres , Fedor G. Pikus , and David Z . Pan . 2011 . High performance lithographic hotspot detection using hierarchically refined machine learning. In Proceedings of the 16th Asia and South Pacific Design Automation Conference. IEEE Press , 775--780. Duo Ding, Andres J. Torres, Fedor G. Pikus, and David Z. Pan. 2011. High performance lithographic hotspot detection using hierarchically refined machine learning. In Proceedings of the 16th Asia and South Pacific Design Automation Conference. IEEE Press, 775--780."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 2009 IEEE International Conference on IC Design and Technology. IEEE, 219--222","author":"Ding Duo","unstructured":"Duo Ding , Xiang Wu , Joydeep Ghosh , and David Z. Pan . 2009. Machine learning based lithographic hotspot detection with critical-feature extraction and classification . In Proceedings of the 2009 IEEE International Conference on IC Design and Technology. IEEE, 219--222 . Duo Ding, Xiang Wu, Joydeep Ghosh, and David Z. Pan. 2009. Machine learning based lithographic hotspot detection with critical-feature extraction and classification. In Proceedings of the 2009 IEEE International Conference on IC Design and Technology. IEEE, 219--222."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1629911.1630053"},{"key":"e_1_2_1_9_1","volume-title":"Pan","author":"Gao Jhih-Rong","year":"2014","unstructured":"Jhih-Rong Gao , Bei Yu , and David Z . Pan . 2014 . Accurate lithography hotspot detection based on PCA-SVM classifier with hierarchical data clustering. In Design-Process-Technology Co-optimization for Manufacturability VIII, Vol. 9053 . International Society for Optics and Photonics , 90530E. Jhih-Rong Gao, Bei Yu, and David Z. Pan. 2014. Accurate lithography hotspot detection based on PCA-SVM classifier with hierarchical data clustering. In Design-Process-Technology Co-optimization for Manufacturability VIII, Vol. 9053. International Society for Optics and Photonics, 90530E."},{"key":"e_1_2_1_10_1","first-page":"832","article-title":"Topology design using squish patterns","volume":"8","author":"Gennari Frank E.","year":"2014","unstructured":"Frank E. Gennari and Ya-Chieh Lai . 2014 . Topology design using squish patterns . US Patent 8 , 832 ,621. Frank E. Gennari and Ya-Chieh Lai. 2014. Topology design using squish patterns. US Patent 8,832,621.","journal-title":"US Patent"},{"key":"e_1_2_1_11_1","volume-title":"Deep Learning","author":"Goodfellow Ian","unstructured":"Ian Goodfellow , Yoshua Bengio , and Aaron Courville . 2016. Deep Learning . MIT Press . Retrieved from http:\/\/www.deeplearningbook.org. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. Retrieved from http:\/\/www.deeplearningbook.org."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317811"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463209.2488817"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_2_1_15_1","volume-title":"Pan","author":"Matsunawa Tetsuaki","year":"2015","unstructured":"Tetsuaki Matsunawa , Jhih-Rong Gao , Bei Yu , and David Z . Pan . 2015 . 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International Society for Optics and Photonics, 97810H. Tetsuaki Matsunawa, Shigeki Nojima, and Toshiya Kotani. 2016. Automatic layout feature extraction for lithography hotspot detection based on deep neural network. In Design-Process-Technology Co-optimization for Manufacturability X, Vol. 9781. International Society for Optics and Photonics, 97810H."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMM.15.2.021009"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.728959"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMM.15.4.043507"},{"key":"e_1_2_1_20_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition","author":"Tong Xiao","year":"2016","unstructured":"Xiao Tong , Hongsheng Li , Wanli Ouyang , and Xiaogang Wang . 2016 . Learning deep feature representations with domain guided dropout for person re-identification . 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Layout hotspot detection with feature tensor generation and deep biased learning. Proceedings of the 54th Annual Design Automation Conference."},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the 24th Asia and South Pacific Design Automation Conference. ACM, 292--298","author":"Ye Wei","unstructured":"Wei Ye , Yibo Lin , Meng Li , Qiang Liu , and David Z. Pan . 2019. LithoROC: Lithography hotspot detection with explicit ROC optimization . In Proceedings of the 24th Asia and South Pacific Design Automation Conference. ACM, 292--298 . Wei Ye, Yibo Lin, Meng Li, Qiang Liu, and David Z. Pan. 2019. LithoROC: Lithography hotspot detection with explicit ROC optimization. In Proceedings of the 24th Asia and South Pacific Design Automation Conference. ACM, 292--298."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2228360.2228576"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 35th International Conference on Computer-Aided Design. 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In Proceedings of the Design Automation Conference."}],"container-title":["ACM Transactions on Design Automation of Electronic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3372044","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3372044","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:19Z","timestamp":1750203859000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3372044"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,19]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,3,31]]}},"alternative-id":["10.1145\/3372044"],"URL":"https:\/\/doi.org\/10.1145\/3372044","relation":{},"ISSN":["1084-4309","1557-7309"],"issn-type":[{"value":"1084-4309","type":"print"},{"value":"1557-7309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,19]]},"assertion":[{"value":"2019-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}