{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:51:15Z","timestamp":1773931875202,"version":"3.50.1"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["BK20232005"],"award-info":[{"award-number":["BK20232005"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62274034"],"award-info":[{"award-number":["62274034"]}],"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":[[2026,7,31]]},"abstract":"<jats:p>With the advancement of technology nodes and the increasingly stringent requirements for stability, general yield analysis of customized circuits in the early stages of design has become a key bottleneck in manufacturing. In this article, we propose a multi-kernel sparse representation-based classification (MKSRC) method to enhance the efficiency and scalability of failure probability estimation by classifying tail samples. It employs class-balanced sampling to address data imbalance issues and utilizes multi-kernel features with adaptive kernel weights to enhance the accuracy and robustness of the classifier. Experimental results on 32-bit SRAM columns and analog circuits demonstrate that the proposed MKSRC method achieves higher classification accuracy and efficiency compared to other state-of-the-art methods, particularly in scenarios with limited training data. Compared to SOTA yield estimation methods, the MKSRC method achieves an average 2.57\u20133.29\u00d7 improvement in both accuracy and efficiency, highlighting its ability to provide efficient and scalable yield analysis solutions for both SRAM and analog circuits.<\/jats:p>","DOI":"10.1145\/3767165","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T11:51:04Z","timestamp":1757677864000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Scalable Yield Analysis of SRAM and Analog Circuits Using Multi-Kernel Sparse Representation"],"prefix":"10.1145","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5736-9536","authenticated-orcid":false,"given":"Ziqi","family":"Wang","sequence":"first","affiliation":[{"name":"National Center of Technology Innovation for EDA, Southeast University","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3118-5074","authenticated-orcid":false,"given":"Liangji","family":"Wu","sequence":"additional","affiliation":[{"name":"National Center of Technology Innovation for EDA, Southeast University","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8405-8936","authenticated-orcid":false,"given":"Xiaoyong","family":"Li","sequence":"additional","affiliation":[{"name":"National Center of Technology Innovation for EDA, Southeast University","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6439-1724","authenticated-orcid":false,"given":"Zhongxi","family":"Guo","sequence":"additional","affiliation":[{"name":"National Center of Technology Innovation for EDA, Southeast University","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1152-0055","authenticated-orcid":false,"given":"Xiao","family":"Shi","sequence":"additional","affiliation":[{"name":"National Center of Technology Innovation for EDA, Southeast University","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0629-7154","authenticated-orcid":false,"given":"Longxing","family":"Shi","sequence":"additional","affiliation":[{"name":"National Center of Technology Innovation for EDA, Southeast University","place":["Nanjing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2020.3044836"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/IEDM.2015.7409677"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2014.2383352"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2005.852295"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/DATE.2010.5456940"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2014.2367234"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2011.5746310"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2007.908005"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2021.3084243"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.2966481"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2014.2336851"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2016.2601606"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3174866"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586136"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/DAC56929.2023.10247952"},{"key":"e_1_3_2_17_2","first-page":"1","volume-title":"Proceedings of the 2020 IEEE\/ACM International Conference On Computer Aided Design (ICCAD)","author":"Shi Xiao","year":"2020","unstructured":"Xiao Shi, Hao Yan, Chuwen Li, Jianli Chen, Longxing Shi, and Lei He. 2020. A non-gaussian adaptive importance sampling method for high-dimensional and multi-failure-region yield analysis. In Proceedings of the 2020 IEEE\/ACM International Conference On Computer Aided Design (ICCAD). 1\u20138."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD57390.2023.10323689"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3649329.3657381"},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Thomas Bengtsson Peter Bickel and Bo Li. 2008. Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems. In Probability and Statistics: Essays in Honor Of David A. Freedman (Ed.). Institute of Mathematical Statistics 316\u2013335.","DOI":"10.1214\/193940307000000518"},{"key":"e_1_3_2_21_2","first-page":"1","volume-title":"Proceedings of the 2014 51st ACM\/EDAC\/IEEE Design Automation Conference (DAC)","author":"Wu Wei","year":"2014","unstructured":"Wei Wu, Wenyao Xu, Rahul Krishnan, Yen-Lung Chen, and Lei He. 2014. REscope: High-dimensional statistical circuit simulation towards full failure region coverage. In Proceedings of the 2014 51st ACM\/EDAC\/IEEE Design Automation Conference (DAC). 1\u20136."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2009.2020721"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3670474.3685954"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3483941"},{"key":"e_1_3_2_25_2","first-page":"283","volume-title":"Proceedings of the 2023 28th Asia and South Pacific Design Automation Conference (ASP-DAC)","author":"Yin Shuo","year":"2023","unstructured":"Shuo Yin, Guohao Dai, and Wei W. Xing. 2023. High-dimensional yield estimation using shrinkage deep features and maximization of integral entropy reduction. In Proceedings of the 2023 28th Asia and South Pacific Design Automation Conference (ASP-DAC). 283\u2013289."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3192361"},{"key":"e_1_3_2_27_2","volume-title":"Proceedings of the Design Automation Conference","author":"Dou Zhenxing","year":"2024","unstructured":"Zhenxing Dou, Ming Cheng, Ming Jia, and Peng Wang. 2024. BNN-YEO: An efficient bayesian neural network for yield estimation and optimization. In Proceedings of the Design Automation Conference. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:273910065"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/SIBGRAPI.2018.00067"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2587119"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.79"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2011.20"},{"key":"e_1_3_2_32_2","unstructured":"Yann Dubois Yangjun Ruan and Chris J. Maddison. 2021. Optimal representations for covariate shifts. In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463104"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3268118"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","unstructured":"Laura Fdez-D\u0131az Sara Glez-Tomillo Elena Monta\u00f1\u00e9s and Jos\u00e9 Ram\u00f3n Quevedo. 2022. Improving importance estimation in covariate shift for providing accurate prediction error. Expert Systems with Applications 193 (2022) 116376.","DOI":"10.1016\/j.eswa.2021.116376"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.858979"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1214\/08-EJS276"}],"container-title":["ACM Transactions on Design Automation of Electronic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3767165","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T12:17:38Z","timestamp":1773922658000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3767165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,19]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7,31]]}},"alternative-id":["10.1145\/3767165"],"URL":"https:\/\/doi.org\/10.1145\/3767165","relation":{},"ISSN":["1084-4309","1557-7309"],"issn-type":[{"value":"1084-4309","type":"print"},{"value":"1557-7309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,19]]},"assertion":[{"value":"2025-03-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-05","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}