Mr. Luyang Luo Profile

Luyang Luo

at Chinese Univ of Hong Kong

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Publications (2)

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SPIE Journal Paper | 24 August 2017

Imbalance aware lithography hotspot detection: a deep learning approach

Haoyu Yang, Luyang Luo, Jing Su, Chenxi Lin, Bei Yu

JM3, Vol. 16, Issue 03, 033504, (August 2017) https://doi.org/10.1117/12.10.1117/1.JMM.16.3.033504

KEYWORDS: Lithography, Neural networks, Convolution, Neurons, Machine learning, Photomasks, Performance modeling, Feature extraction, Convolutional neural networks, Sensors

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With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. We present a deep convolutional neural network (CNN) that targets representative feature learning in lithography hotspot detection. We carefully analyze the impact and effectiveness of different CNN hyperparameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always in the minority in VLSI mask design, the training dataset is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from a high number of false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply hotspot upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model 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 Article | 28 March 2017 Presentation + Paper

Imbalance aware lithography hotspot detection: a deep learning approach

Haoyu Yang, Luyang Luo, Jing Su, Chenxi Lin, Bei Yu

Proceedings Volume 10148, 1014807 (2017) https://doi.org/10.1117/12.2258374

KEYWORDS: Neurons, Photomasks, Convolutional neural networks, Sensors, Very large scale integration, Lithography, Convolution, Neural networks, Machine learning, Feature extraction, Data modeling, Design for manufacturing

Read Abstract +

With the advancement of VLSI technology nodes, light diffraction caused lithographic hotspots have become a serious problem affecting manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with extreme scaling of transistor feature size and more and more complicated layout patterns, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. In this paper, we present a deep convolutional neural network (CNN) targeting representative feature learning in lithography hotspot detection. We carefully analyze impact and effectiveness of different CNN hyper-parameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always minorities in VLSI mask design, the training data set is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from high false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply minority upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves highly 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.

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