Semisupervised Hotspot Detection With Self-Paced Multitask Learning

Lithography simulation is computationally expensive for hotspot detection. Machine learning-based hotspot detection is a promising technique to reduce the simulation overhead. However, most learning approaches rely on a large amount of training data to achieve good accuracy and generality. At the early stage of developing a new technology node, the amount of data with labeled hotspots or nonhotspots is very limited. In this paper, we propose a semisupervised hotspot detection with self-paced multitask learning paradigm, leveraging both data samples with/without labels to improve model accuracy and generality. Experimental results demonstrate that our approach can achieve 4.6%–6.5% better accuracy at the same false alarm levels than the state-of-the-art work using 10%–50% of training data.

[1]  Ronald Rosenfeld,et al.  Semi-supervised learning with graphs , 2005 .

[2]  Yueting Zhuang,et al.  DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection , 2015, IEEE Transactions on Image Processing.

[3]  Chenxi Lin,et al.  Imbalance aware lithography hotspot detection: a deep learning approach , 2017 .

[4]  João P. P. Gomes,et al.  Fast Co-MLM: An Efficient Semi-supervised Co-training Method Based on the Minimal Learning Machine , 2017, New Generation Computing.

[5]  Juhwan Kim,et al.  Hotspot detection on post-OPC layout using full-chip simulation-based verification tool: a case study with aerial image simulation , 2003, SPIE Photomask Technology.

[6]  Seoung Bum Kim,et al.  Consensus rate-based label propagation for semi-supervised classification , 2018, Inf. Sci..

[7]  Tae-Kyun Kim,et al.  Set-based label propagation of face images , 2012, 2012 19th IEEE International Conference on Image Processing.

[8]  Evangeline F. Y. Young,et al.  Layout hotspot detection with feature tensor generation and deep biased learning , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).

[9]  Xiaoyu Song,et al.  Litho-Aware Machine Learning for Hotspot Detection , 2018, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[10]  Nanning Zheng,et al.  Deep self-paced learning for person re-identification , 2017, Pattern Recognit..

[11]  Avrim Blum,et al.  Learning from Labeled and Unlabeled Data using Graph Mincuts , 2001, ICML.

[12]  Yao-Wen Chang,et al.  Recent Research and Emerging Challenges in Physical Design for Manufacturability/Reliability , 2007, 2007 Asia and South Pacific Design Automation Conference.

[13]  Jason Weston,et al.  Curriculum learning , 2009, ICML '09.

[14]  Bei Yu,et al.  Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering , 2014 .

[15]  J. Andres Torres,et al.  High Performance Lithography Hotspot Detection With Successively Refined Pattern Identifications and Machine Learning , 2011, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[16]  J. Andres Torres,et al.  Multi-selection method for physical design verification applications , 2011, Advanced Lithography.

[17]  Jee-Hyong Lee,et al.  Accurate lithography hotspot detection using deep convolutional neural networks , 2016 .

[18]  Wan-Yu Wen,et al.  A novel fuzzy matching model for lithography hotspot detection , 2013, 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC).

[19]  H. Yao,et al.  Efficient Process-Hotspot Detection Using Range Pattern Matching , 2006, 2006 IEEE/ACM International Conference on Computer Aided Design.

[20]  Björn W. Schuller,et al.  Multi-task deep neural network with shared hidden layers: Breaking down the wall between emotion representations , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[21]  Bo Wang,et al.  Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification , 2013, ICCV.

[22]  Iris Hui-Ru Jiang,et al.  Machine-learning-based hotspot detection using topological classification and critical feature extraction , 2013, 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC).

[23]  David Z. Pan,et al.  Optical proximity correction with hierarchical Bayes model , 2015, Advanced Lithography.

[24]  Evangeline F. Y. Young,et al.  Enabling online learning in lithography hotspot detection with information-theoretic feature optimization , 2016, 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[25]  Wan-Yu Wen,et al.  A Fuzzy-Matching Model With Grid Reduction for Lithography Hotspot Detection , 2014, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[26]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[27]  Iris Hui-Ru Jiang,et al.  Accurate process-hotspot detection using critical design rule extraction , 2012, DAC Design Automation Conference 2012.

[28]  Zhi-Hua Zhou,et al.  Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.

[29]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

[30]  J. Andres Torres,et al.  ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite , 2012, 2012 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[31]  Ying Chen,et al.  Semi-supervised hotspot detection with self-paced multi-task learning , 2019, ASP-DAC.

[32]  Shiguang Shan,et al.  Self-Paced Learning with Diversity , 2014, NIPS.

[33]  Lei Zhang,et al.  Active Self-Paced Learning for Cost-Effective and Progressive Face Identification , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Zhiwen Yu,et al.  Semi-Supervised Image Classification With Self-Paced Cross-Task Networks , 2018, IEEE Transactions on Multimedia.

[35]  Iris Hui-Ru Jiang,et al.  Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction , 2015, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[36]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[37]  Andrew B. Kahng,et al.  Fast Dual-Graph-Based Hotspot Filtering , 2008, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[38]  Evangeline F. Y. Young,et al.  Bilinear Lithography Hotspot Detection , 2017, ISPD.

[39]  Zsolt Kira,et al.  Neural network-based clustering using pairwise constraints , 2015, ArXiv.

[40]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[41]  Shigeki Nojima,et al.  Data Efficient Lithography Modeling with Residual Neural Networks and Transfer Learning , 2018, ISPD.