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Proceedings Paper

Efficient approach to early detection of lithographic hotspots using machine learning systems and pattern matching
Author(s): Jen-Yi Wuu; Fedor G. Pikus; Malgorzata Marek-Sadowska
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Paper Abstract

Early lithographic hotspot detection has become increasingly important in achieving lithography-friendly designs and manufacturability closure. Fast physical verification tools employing pattern matching or machine learning techniques have emerged as great options for detecting hotspots in the early design stages. In this work, we propose a characterization methodology that provides measurable quantification of a given hotspot detection tool's capability to capture a previously seen or unseen hotspot pattern. Using this methodology, we conduct a side-by-side comparison of two hotspot detection methods-one using pattern matching and the other based on machine learning. The experimental results reveal that machine learning classifiers are capable of predicting unseen samples but may mispredict some of its training samples. On the other hand, pattern matching-based tools exhibit poorer predictive capability but guarantee full and fast detection on all their training samples. Based on these observations, we propose a hybrid detection solution that utilizes both pattern matching and machine learning techniques. Experimental results show that the hybrid solution combines the strengths of both algorithms and delivers improved detection accuracy while sacrificing little runtime efficiency.

Paper Details

Date Published: 4 April 2011
PDF: 8 pages
Proc. SPIE 7974, Design for Manufacturability through Design-Process Integration V, 79740U (4 April 2011); doi: 10.1117/12.879546
Show Author Affiliations
Jen-Yi Wuu, Univ. of California, Santa Barbara (United States)
Fedor G. Pikus, Mentor Graphics Corp. (United States)
Malgorzata Marek-Sadowska, Univ. of California, Santa Barbara (United States)

Published in SPIE Proceedings Vol. 7974:
Design for Manufacturability through Design-Process Integration V
Michael L. Rieger, Editor(s)

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