{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:30:30Z","timestamp":1773808230112,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Neural network (NN)\u2010based transistor compact modeling is a transformative solution for accelerating device modeling and SPICE circuit simulations. However, conventional NN architectures primarily function as black\u2010box problem solvers. This lack of interpretability significantly limits their capacity to extract and convey meaningful insights into learned data patterns, posing a major barrier to their broader adoption in critical modeling tasks. This work introduces Kolmogorov\u2013Arnold network (KAN) for the transistor\u2014a groundbreaking NN architecture that seamlessly integrates interpretability with high precision in physics\u2010based function modeling. The performance of KAN and Fourier KAN (FKAN) is systematically evaluated for fin field\u2010effect transistor (FinFET) compact modeling, benchmarking them against the golden industry\u2010standard compact model and the widely used multi\u2010layer perceptron architecture. The results reveal that KAN and FKAN consistently achieve superior prediction accuracy for critical figures of merit, including gate current (\n                    <jats:italic>I<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    ), drain charge (\n                    <jats:italic>Q<\/jats:italic>\n                    <jats:sub>D<\/jats:sub>\n                    ), and source charge (\n                    <jats:italic>Q<\/jats:italic>\n                    <jats:sub>S<\/jats:sub>\n                    ). Furthermore, the unique ability of KAN to derive symbolic formulas from learned data patterns is demonstrated and improved\u2014a capability that not only enhances interpretability but also facilitates in\u2010depth transistor analysis and optimization. Additionally, we identify challenges inherent to KAN and FKAN architectures, which limit their ability to achieve a general\u2010purpose SPICE simulation suitability.\n                  <\/jats:p>","DOI":"10.1002\/aisy.202500325","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T12:43:52Z","timestamp":1755261832000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Kolmogorov\u2013Arnold Network for Transistor Compact Modeling"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6632-9804","authenticated-orcid":false,"given":"Rodion","family":"Novkin","sequence":"first","affiliation":[{"name":"TUM School of Computation, Information and Technology, Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Technical University of Munich  85748 Munich Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5649-3102","authenticated-orcid":false,"given":"Hussam","family":"Amrouch","sequence":"additional","affiliation":[{"name":"TUM School of Computation, Information and Technology, Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Technical University of Munich  85748 Munich Germany"}]}],"member":"311","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2012.2198065"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"J. 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