Litho-aware Machine Learning for Hotspot Detection

Authors

Jea Woo Park, Mentor GraphicsFollow
Andres Torres, Mentor Graphics
Xiaoyu Song, Portland State UniversityFollow

Published In

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Document Type

Citation

Publication Date

7-2018

Abstract

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.

Description

©2018 IEEE

Locate the Document

https://doi.org/10.1109/TCAD.2017.2750068

DOI

10.1109/TCAD.2017.2750068

Persistent Identifier

https://archives.pdx.edu/ds/psu/30662

Citation Details

Park, J. W., Torres, A., & Song, X. (2017). Litho-aware machine learning for hotspot detection. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(7), 1510-1514.