极紫外光刻
公制(单位)
随机过程
平版印刷术
随机建模
随机几何学
抵抗
计算机科学
性能指标
算法
生物系统
材料科学
纳米技术
数学
工程类
光电子学
统计
生物
经济
管理
运营管理
图层(电子)
出处
期刊:Journal of Micro-nanolithography Mems and Moems
[SPIE]
日期:2017-12-29
卷期号:16 (04): 1-1
被引量:103
标识
DOI:10.1117/1.jmm.16.4.041013
摘要
Stochastic effects in lithography are usually quantified through local CD variability metrics, such as line-width roughness or local CD uniformity (LCDU), and these quantities have been measured and studied intensively, both in EUV and optical lithography. Next to the CD-variability, stochastic effects can also give rise to local, random printing failures, such as missing contacts or microbridges in spaces. When these occur, there often is no (reliable) CD to be measured locally, and then such failures cannot be quantified with the usual CD-measuring techniques. We have developed algorithms to detect such stochastic printing failures in regular line/space (L/S) or contact- or dot-arrays from SEM images, leading to a stochastic failure metric that we call NOK (not OK), which we consider a complementary metric to the CD-variability metrics. This paper will show how both types of metrics can be used to experimentally quantify dependencies of stochastic effects to, e.g., CD, pitch, resist, exposure dose, etc. As it is also important to be able to predict upfront (in the OPC verification stage of a production-mask tape-out) whether certain structures in the layout are likely to have a high sensitivity to stochastic effects, we look into the feasibility of constructing simple predictors, for both stochastic CD-variability and printing failure, that can be calibrated for the process and exposure conditions used and integrated into the standard OPC verification flow. Finally, we briefly discuss the options to reduce stochastic variability and failure, considering the entire patterning ecosystem.
科研通智能强力驱动
Strongly Powered by AbleSci AI