Hotspot detection using squish-net

Haoyu Yang; Piyush Pathak; Frank Gennari; Ya-Chieh Lai; Bei Yu

doi:10.1117/12.2515172

20 March 2019 Hotspot detection using squish-net

Haoyu Yang, Piyush Pathak, Frank Gennari, Ya-Chieh Lai, Bei Yu

Proceedings Volume 10962, Design-Process-Technology Co-optimization for Manufacturability XIII; 109620S (2019) https://doi.org/10.1117/12.2515172
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

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.

Conference Presentation

Citation Download Citation

Haoyu Yang, Piyush Pathak, Frank Gennari, Ya-Chieh Lai, and Bei Yu "Hotspot detection using squish-net", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620S (20 March 2019); https://doi.org/10.1117/12.2515172

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available