Journal ArticleDOI
Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction
TLDR
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.Abstract:
Because of the widening sub-wavelength lithography gap in advanced fabrication technology, lithography hotspot detection has become an essential task in design for manufacturability. Unlike current state-of-the-art works, which unite pattern matching and machine-learning engines, we fully exploit the strengths of machine learning using novel techniques. By combing topological classification and critical feature extraction, our hotspot detection framework achieves very high accuracy. Furthermore, to speed-up the evaluation, we verify only possible layout clips instead of full-layout scanning. We utilize feedback learning and present redundant clip removal to reduce the false alarm. Experimental results show that the proposed framework is very accurate and demonstrates a rapid training convergence. Moreover, our framework outperforms the 2012 CAD contest at International Conference on Computer-Aided Design (ICCAD) winner on accuracy and false alarm.read more
Citations
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Journal ArticleDOI
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.
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.
Proceedings ArticleDOI
Machine-learning-based hotspot detection using topological classification and critical feature extraction
TL;DR: By combing topological classification and critical feature extraction, this hotspot detection framework achieves very high accuracy and outperforms the 2012 CAD Contest at ICCAD winner on accuracy and false alarm.
Journal ArticleDOI
Online UAV-Mounted Edge Server Dispatching for Mobile-to-Mobile Edge Computing
TL;DR: A novel online unmanned aerial vehicle (UAV)-mounted edge server dispatching scheme is proposed to provide flexible mobile-to-MEC services and can serve more user equipments (UEs) as well as achieve a high resource utilization.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Proceedings ArticleDOI
ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite
TL;DR: 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.
Proceedings ArticleDOI
Accurate process-hotspot detection using critical design rule extraction
TL;DR: This paper proposes an accurate process-hotspot detection framework that extracts only critical design rules to express the topological features of hotspot patterns and adopts a two-stage filtering process to locate all hotspots accurately and efficiently.