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To address this limitation, we propose ForgePattern, a flexible layout pattern generation framework that accommodates diverse task-specific requirements through adaptive selection among customizable reward functions. ForgePattern leverages a customized diffusion model as the generative backbone and incorporates reinforcement learning to directly optimize the diffusion model. By maximizing the expected reward of the reinforcement learning objective, ForgePattern aligns the generated layout patterns with specific requirements without requiring additional data collection or human annotation. Experimental results on challenging layout pattern generation tasks demonstrate that ForgePattern can effectively generate high-quality layout patterns while meeting diverse customization requirements.<\/jats:p>","DOI":"10.1145\/3799991","type":"journal-article","created":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T20:40:18Z","timestamp":1772916018000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["ForgePattern: Flexible Layout Pattern Generation through Reinforcement Learning"],"prefix":"10.1145","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1453-8971","authenticated-orcid":false,"given":"Yu","family":"Feng","sequence":"first","affiliation":[{"name":"Fudan University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6656-3741","authenticated-orcid":false,"given":"Ziyang","family":"Yu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong","place":["Hong Kong, Hong Kong"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1837-5093","authenticated-orcid":false,"given":"Haoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Fudan University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8257-9268","authenticated-orcid":false,"given":"Yiming","family":"Xu","sequence":"additional","affiliation":[{"name":"Fudan University","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6571-2468","authenticated-orcid":false,"given":"Jiaqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhangjiang Laboratory","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-4810","authenticated-orcid":false,"given":"Bei","family":"Yu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong","place":["Hong Kong, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7376-2132","authenticated-orcid":false,"given":"Shisheng","family":"Xiong","sequence":"additional","affiliation":[{"name":"Fudan University","place":["Shanghai, China"]},{"name":"Zhangjiang Laboratory","place":["Shanghai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2593069.2593163"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3175939"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3239559"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3649329.3655680"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2463209.2488818"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2014.2387840"},{"key":"e_1_3_1_8_2","first-page":"30243","article-title":"Lithobench: Benchmarking Ai computational lithography for semiconductor manufacturing","volume":"36","author":"Zheng Su","year":"2023","unstructured":"Su Zheng, Haoyu Yang, Binwu Zhu, Bei Yu, and Martin Wong. 2023. 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