{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lithobench-benchmarking-ai-computational","title":"LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing","arxiv_id":null,"date":"2023-09-26","proceeding":"NeurIPS 2023 11","authors":[],"abstract":"Computational lithography provides algorithmic and mathematical support for resolution enhancement in optical lithography, which is the critical step in semiconductor manufacturing. \nThe time-consuming lithography simulation and mask optimization processes limit the practical application of inverse lithography technology (ILT), a promising solution to the challenges of advanced-node lithography. \nAlthough various machine learning methods for ILT have shown promise for reducing the computational burden, this field is in lack of a dataset that can train the models thoroughly and evaluate the performance comprehensively. \nTo boost the development of AI-driven computational lithography, we present the LithoBench dataset, a collection of circuit layout tiles for deep-learning-based lithography simulation and mask optimization. \nLithoBench consists of more than 120k tiles that are cropped from real circuit designs or synthesized according to the layout topologies of famous ILT testcases. \nThe ground truths are generated by a famous lithography model in academia and an advanced ILT method. \nBased on the data, we provide a framework to design and evaluate deep neural networks (DNNs) with the data. \nThe framework is used to benchmark state-of-the-art models on lithography simulation and mask optimization. \nWe hope LithoBench can promote the research and development of computational lithography. \nLithoBench is available at https://anonymous.4open.science/r/lithobench-APPL.","url_abs":"https://openreview.net/forum?id=JqWtIIaS8n","url_pdf":"https://openreview.net/pdf?id=JqWtIIaS8n","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lithobench-benchmarking-ai-computational","repo_url":"https://github.com/shelljane/lithobench","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}