Title

Litho-aware Machine Learning for Hotspot Detection

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

DOI

10.1109/TCAD.2017.2750068

Persistent Identifier

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

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