{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:03:49Z","timestamp":1781366629590,"version":"3.54.1"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"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":["62202190"],"award-info":[{"award-number":["62202190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003819","name":"Hubei Natural Science Foundation","doi-asserted-by":"crossref","award":["2023AFB237"],"award-info":[{"award-number":["2023AFB237"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Knowledge Innovation Program of Wuhan-Shuguang"},{"name":"Hong Kong Research Grants Council ECS","award":["26208723"],"award-info":[{"award-number":["26208723"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Convolutional neural networks (CNNs) successfully detect lithographic hotspots by learning from hand-designed features of layout patterns or entire layouts, as images, in an end-to-end fashion. However, compared to lithography simulation, CNN-based solutions demonstrate inferior hotspot detection accuracy and a high false-alarm rate. Moreover, the interpretability of the hotspot prediction process has yet to be considered due to the \u201cblack-box\u201d nature of CNNs. In this work, inspired by conventional lithography simulation where defect regions are simulated as direct evidence for hotspot identification, we propose an explainable two-stage CNN-based hotspot detector that considers both the accuracy and interpretability of hotspot detection. Our architecture learns to locate the defect areas in the first stage as extracted hotspot features. In the second stage, we combine the strength of feature engineering and end-to-end learning, incorporating the original layout input, the learned defect location map from the first stage, and a fixed auxiliary region of interest (ROI) map for final hotspot detection. Experimental results for our technique exhibit the highest hotspot accuracy (98.1%) and the lowest false-alarm rate (4.0%) thus far compared to all prior CNN solutions. We also demonstrate the best overall qualitative and quantitative interpretability results with the highest increase in confidence (IC) and the lowest average drop (AD) in scores when CNN interpretation methods such as Grad-CAM-based approaches are applied. We further demonstrate use cases of our technique for successfully justifying and pinpointing hotspot mispredictions by examining the prediction evidence from our learned defect locations.<\/jats:p>","DOI":"10.1145\/3721129","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T14:45:11Z","timestamp":1740667511000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["LithoExp: Explainable Two-stage CNN-based Lithographic Hotspot Detection with Layout Defect Localization"],"prefix":"10.1145","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4961-4604","authenticated-orcid":false,"given":"Cong","family":"Jiang","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7223-6437","authenticated-orcid":false,"given":"Haoyang","family":"Sun","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4674-6006","authenticated-orcid":false,"given":"Dan","family":"Feng","sequence":"additional","affiliation":[{"name":"Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4442-592X","authenticated-orcid":false,"given":"Zhiyao","family":"Xie","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7642-3638","authenticated-orcid":false,"given":"Benjamin","family":"Tan","sequence":"additional","affiliation":[{"name":"Electrical and Software Engineering, University of Calgary, Calgary, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7231-8315","authenticated-orcid":false,"given":"Kang","family":"Liu","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"8969","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Agarwal Chirag","year":"2022","unstructured":"Chirag Agarwal, Marinka Zitnik, and Himabindu Lakkaraju. 2022. Probing GNN explainers: A rigorous theoretical and empirical analysis of GNN explanation methods. In International Conference on Artificial Intelligence and Statistics. PMLR, 8969\u20138996."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.2995338"},{"key":"e_1_3_1_4_2","first-page":"63","volume-title":"Proceedings of the 25th International Conference on Artificial Neural Networks (ICANN\u201916), Part II 25","author":"Binder Alexander","year":"2016","unstructured":"Alexander Binder, Gr\u00e9goire Montavon, Sebastian Lapuschkin, Klaus-Robert M\u00fcller, and Wojciech Samek. 2016. Layer-wise relevance propagation for neural networks with local renormalization layers. In Proceedings of the 25th International Conference on Artificial Neural Networks (ICANN\u201916), Part II 25. Springer, 63\u201371."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8080832"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.3021663"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317824"},{"key":"e_1_3_1_10_2","first-page":"1","volume-title":"2023 Design, Automation & Test in Europe Conference & Exhibition (DATE\u201923)","author":"Chen Zihao","year":"2023","unstructured":"Zihao Chen, Fan Yang, Li Shang, and Xuan Zeng. 2023. Automated and agile design of layout Hotspot detector via neural architecture search. In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE\u201923). IEEE, 1\u20136."},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1145\/2024724.2024902","volume-title":"Proceedings of the 48th Design Automation Conference","author":"Ding Duo","year":"2011","unstructured":"Duo Ding, Jhih-Rong Gao, Kun Yuan, and David Z. Pan. 2011. AENEID: A generic lithography-friendly detailed router based on post-RET data learning and hotspot detection. In Proceedings of the 48th Design Automation Conference. IEEE, 795\u2013800."},{"key":"e_1_3_1_12_2","first-page":"775","volume-title":"16th Asia and South Pacific Design Automation Conference (ASP-DAC\u201911)","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 16th Asia and South Pacific Design Automation Conference (ASP-DAC\u201911). IEEE, 775\u2013780."},{"key":"e_1_3_1_13_2","first-page":"13","volume-title":"Modeling Aspects in Optical Metrology II","author":"Erdmann Andreas","year":"2009","unstructured":"Andreas Erdmann, Tim F\u00fchner, Feng Shao, and Peter Evanschitzky. 2009. Lithography simulation: Modeling techniques and selected applications. In Modeling Aspects in Optical Metrology II, Vol. 7390. SPIE, 13\u201329."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467283"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3135786"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3112637"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3610293"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3187455"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3089943"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.3015918"},{"key":"e_1_3_1_21_2","first-page":"424","volume-title":"Proceedings of the 11th European Conference on Computer Vision (ECCV\u201910)Computer Vision, Part IV 11","author":"Ladick\u1ef3 L\u2019ubor","year":"2010","unstructured":"L\u2019ubor Ladick\u1ef3, Paul Sturgess, Karteek Alahari, Chris Russell, and Philip H. S. Torr. 2010. What, where and how many? Combining object detectors and CRFs. In Proceedings of the 11th European Conference on Computer Vision (ECCV\u201910)Computer Vision, Part IV 11. Springer, 424\u2013437."},{"key":"e_1_3_1_22_2","first-page":"1233","volume-title":"2018 IEEE International Conference on Data Mining Workshops (ICDMW\u201918)","author":"Li Biao","year":"2018","unstructured":"Biao Li, Yong Shi, Zhiquan Qi, and Zhensong Chen. 2018. A survey on semantic segmentation. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW\u201918). IEEE, 1233\u20131240."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.3033749"},{"issue":"5","key":"e_1_3_1_24_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3408288","article-title":"Adversarial perturbation attacks on ML-based CAD: A case study on CNN-based lithographic hotspot detection","volume":"25","author":"Liu Kang","year":"2020","unstructured":"Kang Liu, Haoyu Yang, Yuzhe Ma, Benjamin Tan, Bei Yu, Evangeline F. Y. Young, Ramesh Karri, and Siddharth Garg. 2020. Adversarial perturbation attacks on ML-based CAD: A case study on CNN-based lithographic hotspot detection. ACM Transactions on Design Automation of Electronic Systems (TODAES) 25, 5 (2020), 1\u201331.","journal-title":"ACM Transactions on Design Automation of Electronic Systems (TODAES)"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_1_26_2","unstructured":"Adam McCaughan and Lucas H. Gabrielli. 2023. gdspy. (April2023). https:\/\/gdspy.readthedocs.io\/en\/stable\/"},{"key":"e_1_3_1_27_2","first-page":"1","volume-title":"Optical Microlithography XVIII","author":"Mack Chris A.","year":"2005","unstructured":"Chris A. Mack. 2005. Thirty years of lithography simulation. In Optical Microlithography XVIII, Vol. 5754. SPIE, 1\u201312."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2010.12.011"},{"key":"e_1_3_1_29_2","first-page":"201","volume-title":"Design-Process-Technology Co-optimization for Manufacturability IX","author":"Matsunawa Tetsuaki","year":"2015","unstructured":"Tetsuaki Matsunawa, Jhih-Rong Gao, Bei Yu, and David Z. Pan. 2015. A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction. In Design-Process-Technology Co-optimization for Manufacturability IX, Vol. 9427. SPIE, 201\u2013211."},{"issue":"7","key":"e_1_3_1_30_2","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee Shervin","year":"2021","unstructured":"Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos. 2021. Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 7 (2021), 3523\u20133542.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_31_2","article-title":"Rise: Randomized input sampling for explanation of black-box models","author":"Petsiuk Vitali","year":"2018","unstructured":"Vitali Petsiuk, Abir Das, and Kate Saenko. 2018. Rise: Randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421 (2018).","journal-title":"arXiv preprint arXiv:1806.07421"},{"key":"e_1_3_1_32_2","first-page":"1","volume-title":"2018 IEEE 36th VLSI Test Symposium (VTS\u201918)","author":"Reddy Gaurav Rajavendra","year":"2018","unstructured":"Gaurav Rajavendra Reddy, Constantinos Xanthopoulos, and Yiorgos Makris. 2018. Enhanced hotspot detection through synthetic pattern generation and design of experiments. In 2018 IEEE 36th VLSI Test Symposium (VTS\u201918). IEEE, 1\u20136."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_1_36_2","unstructured":"Siemens. 2019. Calibre-LFD. (October2019). https:\/\/www.mentor.com\/products\/ic_nanometer_design\/design-for-manufacturing\/calibre-lfd"},{"key":"e_1_3_1_37_2","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan Karen","year":"2013","unstructured":"Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013).","journal-title":"arXiv preprint arXiv:1312.6034"},{"key":"e_1_3_1_38_2","article-title":"Smoothgrad: Removing noise by adding noise","author":"Smilkov Daniel","year":"2017","unstructured":"Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Vi\u00e9gas, and Martin Wattenberg. 2017. Smoothgrad: Removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017).","journal-title":"arXiv preprint arXiv:1706.03825"},{"key":"e_1_3_1_39_2","first-page":"1233","volume-title":"2022 Design, Automation & Test in Europe Conference & Exhibition (DATE\u201922)","author":"Sun Shuyuan","year":"2022","unstructured":"Shuyuan Sun, Yiyang Jiang, Fan Yang, Bei Yu, and Xuan Zeng. 2022. Efficient hotspot detection via graph neural network. In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE\u201922). IEEE, 1233\u20131238."},{"key":"e_1_3_1_40_2","article-title":"Gradients of counterfactuals","author":"Sundararajan Mukund","year":"2016","unstructured":"Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2016. Gradients of counterfactuals. arXiv preprint arXiv:1611.02639 (2016).","journal-title":"arXiv preprint arXiv:1611.02639"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/2429384.2429457"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2014.2351273"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586273"},{"issue":"3","key":"e_1_3_1_45_2","first-page":"033504","article-title":"Imbalance aware lithography hotspot detection: A deep learning approach","volume":"16","author":"Yang Haoyu","year":"2017","unstructured":"Haoyu Yang, Luyang Luo, Jing Su, Chenxi Lin, and Bei Yu. 2017. Imbalance aware lithography hotspot detection: A deep learning approach. Journal of Micro\/Nanolithography, MEMS, and MOEMS 16, 3 (2017), 033504\u2013033504.","journal-title":"Journal of Micro\/Nanolithography, MEMS, and MOEMS"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287624.3288747"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcad.2018.2837078"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/2228360.2228576"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/2463209.2488816"},{"key":"e_1_3_1_50_2","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops","author":"Zee Timothy","year":"2019","unstructured":"Timothy Zee, Geeta Gali, and Ifeoma Nwogu. 2019. Enhancing human face recognition with an interpretable neural network. In Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops. IEEE."},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/2966986.2967032"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD51958.2021.9643590"}],"container-title":["ACM Transactions on Design Automation of Electronic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3721129","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3721129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:57:39Z","timestamp":1750298259000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3721129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,19]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5,31]]}},"alternative-id":["10.1145\/3721129"],"URL":"https:\/\/doi.org\/10.1145\/3721129","relation":{},"ISSN":["1084-4309","1557-7309"],"issn-type":[{"value":"1084-4309","type":"print"},{"value":"1557-7309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,19]]},"assertion":[{"value":"2024-04-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}