{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:09:47Z","timestamp":1775326187088,"version":"3.50.1"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFB2202702"],"award-info":[{"award-number":["2018YFB2202702"]}],"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":[[2022,3,31]]},"abstract":"<jats:p>\n            \u201cCurse of dimensionality\u201d has become the major challenge for existing high-sigma yield analysis methods. In this article, we develop a meta-model using\n            <jats:bold>Low-Rank Tensor Approximation (LRTA)<\/jats:bold>\n            to substitute expensive SPICE simulation. The polynomial degree of our LRTA model grows linearly with the circuit dimension. This makes it especially promising for high-dimensional circuit problems. Our LRTA meta-model is solved efficiently with a robust greedy algorithm and calibrated iteratively with a bootstrap-assisted adaptive sampling method. We also develop a novel global sensitivity analysis approach to generate a reduced LRTA meta-model which is more compact. It further accelerates the procedure of model calibration and yield estimation. Experiments on memory and analog circuits validate that the proposed LRTA method outperforms other state-of-the-art approaches in terms of accuracy and efficiency.\n          <\/jats:p>","DOI":"10.1145\/3483941","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T20:19:32Z","timestamp":1635884372000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["A Compact High-Dimensional Yield Analysis Method using Low-Rank Tensor Approximation"],"prefix":"10.1145","volume":"27","author":[{"given":"Xiao","family":"Shi","sequence":"first","affiliation":[{"name":"Southeast University, Sipailou, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yan","sequence":"additional","affiliation":[{"name":"Southeast University, Sipailou, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiancun","family":"Huang","sequence":"additional","affiliation":[{"name":"Southeast University, Sipailou, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengzhen","family":"Xuan","sequence":"additional","affiliation":[{"name":"Southeast University, Sipailou, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"He","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longxing","family":"Shi","sequence":"additional","affiliation":[{"name":"Southeast University, Sipailou, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1137\/13091899X"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2019.2925340"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/1509456.1509533"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1214\/009053604000000067"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317818"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/1146909.1146930"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2016.06.005"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.5555\/1870926.1871123"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2009.09.018"},{"key":"e_1_3_1_11_2","volume-title":"Global Sensitivity Analysis: the Primer","author":"Saltelli Andrea","year":"2008","unstructured":"Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, and Stefano Tarantola. 2008. 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