{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T08:05:24Z","timestamp":1767773124997,"version":"3.41.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"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":["62141408 62141407 and 61929102"],"award-info":[{"award-number":["62141408 62141407 and 61929102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2020YFA0711900 and 2020YFA0711901"],"award-info":[{"award-number":["2020YFA0711900 and 2020YFA0711901"]}],"id":[{"id":"10.13039\/501100012166","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":[[2024,1,31]]},"abstract":"<jats:p>\n            The reliability of circuits is significantly affected by process variations in manufacturing and environmental variation during operation. Current yield optimization algorithms take process variations into consideration to improve circuit reliability. However, the influence of environmental variations (e.g., voltage and temperature variations) is often ignored in current methods because of the high computational cost. In this article, a novel and efficient approach named\n            <jats:italic>BNN-BYO<\/jats:italic>\n            is proposed to optimize the yield of analog circuits in multiple environmental corners. First, we use a Bayesian Neural Network (BNN) to simultaneously model the yields and performances of interest in multiple corners efficiently. Next, the multi-corner yield optimization can be performed by embedding BNN into a Bayesian optimization framework. Since the correlation among yields and performances of interest in different corners is implicitly encoded in the BNN model, it provides great modeling capabilities for yields and their uncertainties to improve the efficiency of yield optimization. Our experimental results demonstrate that the proposed method can save up to 45.3% of simulation cost compared to other baseline methods to achieve the same target yield. In addition, for the same simulation cost, our proposed method can find better design points with 3.2% yield improvement.\n          <\/jats:p>","DOI":"10.1145\/3626321","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T15:45:14Z","timestamp":1696607114000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Yield Optimization for Analog Circuits over Multiple Corners via Bayesian Neural Networks: Enhancing Circuit Reliability under Environmental Variation"],"prefix":"10.1145","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3681-9144","authenticated-orcid":false,"given":"Nanlin","family":"Guo","sequence":"first","affiliation":[{"name":"Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2187-8296","authenticated-orcid":false,"given":"Fulin","family":"Peng","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3733-9895","authenticated-orcid":false,"given":"Jiahe","family":"Shi","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2164-8175","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8742-687X","authenticated-orcid":false,"given":"Jun","family":"Tao","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8097-4053","authenticated-orcid":false,"given":"Xuan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"20","volume-title":"Proceedings of the European Conference on Circuit Theory and Design","author":"Gielen G.","year":"2007","unstructured":"G. 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