Paper
23 March 2020 A feature selection method for weak classifier based hotspot detection
Author Affiliations +
Abstract
As VLSI device feature sizes are getting smaller and smaller, lithography hotspot detection and elimination have become more important to avoid yield loss. Although various machine learning based methods have been proposed, it is not easy to find appropriate parameters to achieve high accuracy. This paper proposes a feature selection method by using the probability distributions of layout features. Our method enables automatic feature optimization and classifier construction. It can be adaptive to different layout patterns with various features. In order to evaluate hotspot detection methods in the situation close to actual problem, dataset based on ICCAD2019 dataset is used for evaluation. Experimental results show the effectiveness of our method and limitations.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hidekazu Takahashi, Hiroki Ogura, Shimpei Sato, Atsushi Takahashi, and Chikaaki Kodama "A feature selection method for weak classifier based hotspot detection", Proc. SPIE 11328, Design-Process-Technology Co-optimization for Manufacturability XIV, 113281E (23 March 2020); https://doi.org/10.1117/12.2559358
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KEYWORDS
Feature extraction

Feature selection

Lithography

Simulation of CCA and DLA aggregates

Machine learning

Manufacturing

Computer programming

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