Litho-Aware Machine Learning for Hotspot Detection

被引：17

作者：

Park, Jea Woo ^{[1
,2
]}

Torres, Andres ^{[1
]}

Song, Xiaoyu ^{[2
]}

机构：

[1] Mentor Graph Corp, Wilsonville, OR 97070 USA

[2] Portland State Univ, Dept Elect & Comp Engn, Portland, OR 97201 USA

来源：

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS | 2018年 / 37卷 / 07期

关键词：

Design for manufacturability; hotspot detection; lithography; machine learning (ML); pattern match; process hotspot; support vector machine (SVM);

D O I：

10.1109/TCAD.2017.2750068

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose a novel methodology for machine learning-based hotspot detection that uses lithography information to build support vector machine (SVM) during its learning process. Unlike previous studies that use only geometric information or require a post-optical proximity correction (OPC) mask, this proposed method utilizes detailed optical information but bypasses post-OPC mask by sampling latent image intensity and uses those points to train an SVM model. The results suggest high accuracy and low false alarm, and faster runtime compared with methods that require a post-OPC mask.

引用

页码：1510 / 1514

页数：5

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