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Syst."],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>\n            We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed \u223c500 hours of computation. On the other hand, if 10\n            <jats:sup>6<\/jats:sup>\n            samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken \u223c250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and\/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.\n          <\/jats:p>","DOI":"10.1145\/3440014","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T17:17:11Z","timestamp":1610558231000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine Learning for Statistical Modeling"],"prefix":"10.1145","volume":"26","author":[{"given":"Urmimala","family":"Roy","sequence":"first","affiliation":[{"name":"Microelectronics Research Center, The University of Texas at Austin, Austin, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanmoy","family":"Pramanik","sequence":"additional","affiliation":[{"name":"Microelectronics Research Center, The University of Texas at Austin, Austin, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subhendu","family":"Roy","sequence":"additional","affiliation":[{"name":"Cadence Design Systems, San Jose, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Avhishek","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leonard F.","family":"Register","sequence":"additional","affiliation":[{"name":"Microelectronics Research Center, The University of Texas at Austin, Austin, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay K.","family":"Banerjee","sequence":"additional","affiliation":[{"name":"Microelectronics Research Center, The University of Texas at Austin, Austin, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.367113"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMAG.2012.2209122"},{"key":"e_1_2_1_3_1","unstructured":"Cadence Design Systems. 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