Post Exposure Bake Physics-informed Fourier Laplace Neural Network for Post Bake Simulation

Post Exposure Bake Physics-informed Fourier Laplace Neural Network for Post Bake Simulation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 August 2025 V1 Latest version Share on Post Exposure Bake Physics-informed Fourier Laplace Neural Network for Post Bake Simulation Authors : Le Ma , Lisong Dong , and Yayi Wei [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175522171.10232782/v1 133 views 88 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Post exposure bake (PEB) is a widely used technique in advanced lithography, with its simulation being a critical step in the lithography simulation. PEB simulation involves solving several complex partial differential equations, leading to substantial computational time consumption that hinders its application to full-chip simulation. Deep learning (DL) methods have demonstrated great potential in accelerating PEB simulation. However, the lack of domain knowledge of PEB limits the ability of DL-based approaches to enhance simulation accuracy. In this paper, we propose a post exposure bake physics-informed Fourier Laplace neural network (PBFLN) for PEB simulation. This approach utilizes GPU-enabled PEB simulation as a layer within the Fourier Laplace neural network framework and iteratively conducts the prediction and correction of PEB reactant production distribution. With this approach, the critical dimension (CD) error of the resist profile between the commercial rigorous simulation tool and DL-based method is decreased by 45% compared to the previous state-of-the-art method, and computation time is reduced by a few orders of magnitude compared to commercial rigorous simulation tools. By gearing up DL with the physical knowledge of PEB, PBFLN provides a novel and efficient PEB simulation solution. Supplementary Material File (post exposure bake physics-informed fourier laplace neural network for post bake simulation.docx) Download 3.29 MB Information & Authors Information Version history V1 Version 1 15 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning fourier laplace neural network photoresist post exposure bake Authors Affiliations Le Ma State Key Laboratory of Fabrication Technologies for Integrated Circuits View all articles by this author Lisong Dong State Key Laboratory of Fabrication Technologies for Integrated Circuits View all articles by this author Yayi Wei [email protected] State Key Laboratory of Fabrication Technologies for Integrated Circuits View all articles by this author Metrics & Citations Metrics Article Usage 133 views 88 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Le Ma, Lisong Dong, Yayi Wei. Post Exposure Bake Physics-informed Fourier Laplace Neural Network for Post Bake Simulation. Authorea . 15 August 2025. DOI: https://doi.org/10.22541/au.175522171.10232782/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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