On Improving Hotspot Detection Through Synthetic Pattern-Based Database Enhancement

Design hotspots are layout patterns which may cause defects due to complex design and process interactions. Several machine learning and pattern matching-based methods have been proposed to identify and correct them early during design stages. However, almost all of them suffer from high false-alarm...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems 2021-12, Vol.40 (12), p.2522-2527
Main Authors: Reddy, Gaurav Rajavendra, Xanthopoulos, Constantinos, Makris, Yiorgos
Format: Article
Language:English
Subjects:
Database enhancement
Design defects
design for manufacturability
Design for manufacture
Design of experiments
False alarms
Feature extraction
Image edge detection
Layout
lithographic hotspot detection
Lithography
Machine learning
Pattern formation
Pattern generation
Pattern matching
physical design
synthetic pattern generation
Transforms
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Summary:Design hotspots are layout patterns which may cause defects due to complex design and process interactions. Several machine learning and pattern matching-based methods have been proposed to identify and correct them early during design stages. However, almost all of them suffer from high false-alarm rates, mainly because they are oblivious to the root causes of hotspots. In this work, we seek to address this limitation by using a novel database enhancement approach through synthetic pattern generation based on a carefully crafted design of experiments. We evaluate the effectiveness of the proposed method using industry-standard tools and designs and demonstrate more than 3\times reduction in classification error in comparison to the state-of-the-art.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2021.3049285