Machine learning for mask/wafer hotspot detection and mask synthesis

被引：19

作者：

Lin, Yibo ^{[1
]}

Xu, Xiaoqing ^{[1
]}

Ou, Jiaojiao ^{[1
]}

Pan, David Z. ^{[1
]}

机构：

[1] Univ Texas Austin, ECE Dept, Austin, TX 78712 USA

来源：

PHOTOMASK TECHNOLOGY 2017 | 2017年 / 10451卷

关键词：

OPTICAL PROXIMITY CORRECTION;

D O I：

10.1117/12.2282943

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Machine learning is a powerful computer science technique that can derive knowledge from big data and make predictions/decisions. Since nanometer integrated circuits (IC) and manufacturing have extremely high complexity and gigantic data, there is great opportunity to apply and adapt various machine learning techniques in IC physical design and verification. This paper will first give an introduction to machine learning, and then discuss several applications, including mask/wafer hotspot detection, and machine learning-based optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion. We will further discuss some challenges and research directions.

引用

页数：13