Source mask optimization (SMO) at full chip scale using inverse lithography technology (ILT) based on level set methods

Lithography Asia Pub Date : 2009-12-03 DOI:10.1117/12.843578
L. Pang, Peter Hu, Danping Peng, Dongxue Chen, T. Cecil, Lin He, G. Xiao, V. Tolani, Thuc H. Dam, Kiho Baik, B. Gleason
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引用次数: 24

Abstract

For semiconductor manufacturers moving toward advanced technology nodes -32nm, 22nm and below - lithography presents a great challenge, because it is fundamentally constrained by basic principles of optical physics. For years, source optimization and mask pattern correction have been conducted as two separate RET steps. For source optimization, the source was optimized based on fixed mask patterns; in other words, OPC and SRAFs were not considered during source optimization. Recently, some new approaches to Source Mask Optimization (SMO) have been introduced for the lithography development stage. The next important step would be the extension of SMO, and in particular the mask optimization in SMO, into full chip. In this paper, a computational framework based on Level Set Method is presented that enables simultaneous source and mask optimization (using Inverse Lithography Technology, or ILT), and can extend the SMO from single clip, to multiple clips, all the way to full chip. Memory and logic device results at the 32nm node and below are presented which demonstrate the benefits of this level-set-method-based SMO and its extendibility to full chip designs.
基于水平集方法的逆光刻技术全芯片源掩模优化(SMO)
对于半导体制造商来说,向先进的技术节点-32nm, 22nm及以下-光刻提出了一个巨大的挑战,因为它从根本上受到光学物理基本原理的限制。多年来,源优化和掩模模式校正一直作为两个独立的RET步骤进行。在源优化方面,基于固定掩模模式对源进行优化;换句话说,在源优化过程中没有考虑OPC和srf。近年来,光刻技术发展阶段引入了一些新的源掩模优化方法。下一个重要的步骤将是SMO的扩展,特别是SMO中的掩模优化,到全芯片。本文提出了一种基于水平集方法的计算框架,该框架可以同时实现源和掩模优化(使用逆光刻技术,ILT),并可以将SMO从单剪辑扩展到多剪辑,一直扩展到全芯片。存储器和逻辑器件在32nm及以下节点上的结果显示了这种基于电平集方法的SMO的优点及其在全芯片设计中的可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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