Compressive Sensing Approaches for Lithographic Source and Mask Joint Optimization

• 作者: Gonzalo R.Arce Xu Ma Zhiqiang Wang
• 来源期刊: 微电子制造学报
• 学科: 工学
• 关键词: Computational LITHOGRAPHY SOURCE MASK optimization(SMO) COMPRESSIVE sensing(CS) INVERSE problem
• 年，卷（期）: 2018,（2）
• 页码范围: 6-12
• 页数: 7页
• 分类号: TN

摘要

Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency has become one of the most challenging issues for the development of pixelated SMO techniques.Recently,compressive sensing(CS)theory has be explored in the area of computational inverse problems.This paper proposes a CS approach to improve the computational efficiency of pixel-based SMO algorithms.To our best knowledge,this paper is the first to develop fast SMO algorithms based on the CS framework.The SMO workflow can be separated into two stages,i.e.,source optimization(SO)and mask optimization(MO).The SO and MO are formulated as the linear CS and nonlinear CS reconstruction problems,respectively.Based on the sparsity representation of the source and mask patterns on the predefined bases,the SO and MO procedures are implemented by sparse image reconstruction algorithms.A set of simulations are presented to verify the proposed CS-SMO methods.The proposed CS-SMO algorithms are shown to outperform the traditional gradient-based SMO algorithm in terms of both computational efficiency and lithography imaging performance.