Computational Lithography Using Machine Learning Models

• Published in: IPSJ Transactions on System LSI Design Methodology Vol. 14; pp. 2 - 10
• Main Author: Shin, Youngsoo
• Format: Journal Article
• Language: English
• Published: Tokyo Information Processing Society of Japan 2021; Japan Science and Technology Agency
• Subjects: assist features; computational lithography; hotspot; Lithography; lithography model; lithography test patterns; Machine learning; Model accuracy; Model testing; optical proximity correction; Optimization
• ISSN: 1882-6687; 1882-6687
• DOI: 10.2197/ipsjtsldm.14.2

Machine learning models have been applied to a wide range of computational lithography applications since around 2010. They provide higher modeling capability, so their application allows modeling of higher accuracy. Many applications which are computationally expensive can take advantage of machine learning models, since a well trained model provides a quick estimation of outcome. This tutorial reviews a number of such computational lithography applications that have been using machine learning models. They include mask optimization with OPC (optical proximity correction) and EPC (etch proximity correction), assist features insertion and their printability check, lithography modeling with optical model and resist model, test patterns, and hotspot detection and correction.