{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:51:09Z","timestamp":1777107069866,"version":"3.51.4"},"reference-count":199,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"name":"Major Key Project of PCL","award":["PCL2025AS04"],"award-info":[{"award-number":["PCL2025AS04"]}]},{"name":"NSF of Fujian Province","award":["2024j09045"],"award-info":[{"award-number":["2024j09045"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>The increasing complexity of digital circuits and the limitations of heuristic methods have led to growing interest in applying Machine Learning (ML) to Logic Synthesis (LS). ML provides a promising paradigm shift by implementing automated, scalable, and data-driven optimization strategies. This survey provides a comprehensive overview of the latest studies on ML approaches in LS, offering a deep understanding of the fundamental ML methods, and analyzing their strengths and limitations through systematic comparisons. We categorize existing works into two main types: ML-Assistance methods aiming at predicting performance metrics and reducing the cost of traditional simulations, and ML-Agent methods directly replacing heuristic processes in the LS flow. We further analyze ML methods and applications in different LS stages, including Boolean circuit generation, Boolean circuit analysis, logic optimization, and technology mapping, showing great achievements and improvement in exploring non-linear design spaces and discovering new optimization strategies. Finally, we discuss the challenges and limitations in the current situation and further provide a vision of future directions in LS.<\/jats:p>\n                  <jats:p\/>","DOI":"10.1145\/3785362","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T11:08:02Z","timestamp":1765796882000},"page":"1-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Survey of Machine Learning Approaches in Logic Synthesis"],"prefix":"10.1145","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9075-8007","authenticated-orcid":false,"given":"Miao","family":"Liu","sequence":"first","affiliation":[{"name":"University of the Chinese Academy of Sciences","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7525-9375","authenticated-orcid":false,"given":"Liwei","family":"Ni","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8205-4564","authenticated-orcid":false,"given":"Junfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5787-0101","authenticated-orcid":false,"given":"Xingyu","family":"Meng","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1620-6889","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7512-5746","authenticated-orcid":false,"given":"Xiaoze","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6514-2151","authenticated-orcid":false,"given":"Xinhua","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, University of the Chinese Academy of Sciences","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7145-9391","authenticated-orcid":false,"given":"Xingquan","family":"Li","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3994-1401","authenticated-orcid":false,"given":"Jungang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, University of the Chinese Academy of Sciences","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2022. 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