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Proceedings Paper

Hotspot detection using squish-net
Author(s): Haoyu Yang; Piyush Pathak; Frank Gennari; Ya-Chieh Lai; Bei Yu
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Paper Abstract

Design-process weakpoints also known as hotspots cause systematic yield loss in semiconductor manufacturing. One of the main goals of DFM is to detect such hotspots. For the application of AI in hotspot detection, a variety of machine learning-based techniques have been proposed as an alternative to time expensive process simulations. Related research works range from finding efficient layout representations and features and developing reliable machine learning models. Main stream layout representations include density-based feature, pixel-based feature, frequency domain feature, concentric circle sampling (CCS) and squish pattern. However most of them are either suffering from information loss (e.g. density-based feature, and CCS), or not storage efficient (e.g. images). To address these problems, we propose a convolutional neural network called Squish-Net where the input pattern representation is in an adaptive squish form. Here, the squish pattern representation is modified to handle variations in the topological complexity across a pattern catalog, which still allows no information loss and high data compression. We show that different labeling strategies and pattern radius contribute to the trade-offs between prediction accuracy and model precision. Two imbalance-aware training strategies are also discussed with supporting experiments.

Paper Details

Date Published: 20 March 2019
PDF: 8 pages
Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620S (20 March 2019); doi: 10.1117/12.2515172
Show Author Affiliations
Haoyu Yang, The Chinese Univ. of Hong Kong (United States)
Cadence Design Systems Inc. (United States)
Piyush Pathak, Cadence Design Systems, Inc. (United States)
Frank Gennari, Cadence Design Systems, Inc. (United States)
Ya-Chieh Lai, Cadence Design Systems, Inc. (United States)
Bei Yu, The Chinese Univ. of Hong Kong (Hong Kong, China)

Published in SPIE Proceedings Vol. 10962:
Design-Process-Technology Co-optimization for Manufacturability XIII
Jason P. Cain, Editor(s)

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