Proceedings ArticleDOI
ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite
J. Andres Torres
- pp 349-350
TLDR
This contest is aimed to provide a suite of layouts which highlight the challenges of this application: Widely different classes, limited amount of data and low prediction rates.Abstract:
With the widespread adoption of design for manufacturing techniques and design and process co-optimization as well as the increase in the complexity of the processes to manufacture integrated circuits there is pressing need in finding quickly to calibrate yet accurate and high performing methods to identify layout topologies which may cause yield loss. While full-based simulations provide the most accurate prediction possible their runtime prohibits an adoption at all levels of the design flow. Alternative traditional rule checking including pattern matching techniques are fast but have a limited application in finding locations that were not part the training set. Several approaches to improve the accuracy of the prediction to reduce the number of miss structures and false detections have been proposed, but none have yielded and acceptable tradeoff between accuracy and runtime. This contest is aimed to provide a suite of layouts which highlight the challenges of this application: Widely different classes, limited amount of data and low prediction rates.read more
Citations
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Design for Manufacturing With Emerging Nanolithography
TL;DR: This paper surveys key design for manufacturing issues for extreme scaling with emerging nanolithography technologies, including double/multiple patterning lithography, extreme ultraviolet lithographic, and electron-beam lithography.
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Imbalance aware lithography hotspot detection: a deep learning approach
TL;DR: A deep convolutional neural network that targets representative feature learning in lithography hotspot detection and achieves comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.
Journal ArticleDOI
Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction
TL;DR: This work utilizes feedback learning and present redundant clip removal to reduce the false alarm and outperforms the 2012 CAD contest at International Conference on Computer-Aided Design (ICCAD) winner on accuracy and false alarm.
Proceedings ArticleDOI
Enabling online learning in lithography hotspot detection with information-theoretic feature optimization
TL;DR: A unified machine learning based hotspot detection framework, where feature extraction and optimization is guided by an information-theoretic approach and solved by a dynamic programming model, which can be naturally extended to online learning scenario.
Proceedings ArticleDOI
Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning
TL;DR: A deep learning framework for high performance and large scale hotspot detection is developed and a biased learning algorithm is proposed to train the convolutional neural network to further improve detection accuracy with small false alarm penalties.
References
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Proceedings ArticleDOI
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