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The capability of the technique was investigated on a test multiplier, which was developed for future use in machine learning (ML) accelerators whose convergence is sensitive to process\u2010variation\u2010induced DC offsets. By adjusting the multiplier biasing voltages, the proposed Bayesian\u2010optimization\u2010based method was able to reduce the offset within \u00b11.8\u2009mV from an uncorrected maximum offset of 10.6\u2009mV. In addition to reducing offsets, the measurements of the 65\u2010nm CMOS multiplier also showed an average linearity\u2010error improvement of nearly 10%, from 12.2% prior to offset correction to 2.2% after correction. We demonstrate that the proposed offset correction improved the learning outcome accuracy for MNIST dataset digit classification from approximately 10% to 90%.<\/jats:p>","DOI":"10.1049\/cds2\/5591883","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T15:36:06Z","timestamp":1761924966000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian\u2010Optimization\u2010Based Post\u2010Silicon Offset\u2010Cancelation Technique for Analog Multipliers"],"prefix":"10.1049","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7244-5676","authenticated-orcid":false,"given":"Thisara","family":"Kulatunga","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4794-9849","authenticated-orcid":false,"given":"Svetlana","family":"Yanushkevich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5228-6907","authenticated-orcid":false,"given":"Leonid","family":"Belostotski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"crossref","unstructured":"WeberR. 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