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A Fuzzy Matching Model with Dimensionality Reduction for Lithography Hotspot Detection
Thesis

A Fuzzy Matching Model with Dimensionality Reduction for Lithography Hotspot Detection

Chen, Jing-Yi
Masters, 國立清華大學, 資訊工程學系
2012

Abstract

可製造性設計 熱點偵測 光微影熱點 機器學習 模糊比對 降維技術 Design for Manufacturability Hotspot Detection Lithography Hotspot Machine Learning Fuzzy Matching Dimensionality Reduction
In advanced IC manufacturing, as the gap between lithography optical wavelength and feature size increases, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which can dynamically tune appropriate fuzzy regions around known hotspots. Based on this model, we develop a fast algorithm for lithography hotspot detection with very low chances of false-alarm. For further improvement, a dimensionality reduction is applied to extract representative features in the high dimensional space. Our results are very encouraging with average 77.9% accuracy and 992.2 false-alarm counts.

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