Proceedings ArticleDOI
Enabling online learning in lithography hotspot detection with information-theoretic feature optimization
Hang Zhang,Bei Yu,Evangeline F. Y. Young +2 more
- pp 47
TLDR
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.Abstract:
With the continuous shrinking of technology nodes, lithography hotspot detection and elimination in the physical verification phase is of great value. Recently machine learning and pattern matching based methods have been extensively studied to overcome runtime overhead problem of expensive full-chip lithography simulation. However, there is still much room for improvement in terms of accuracy and Overall Detection and Simulation Time (ODST). In this paper, we propose 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. More importantly, our framework can be naturally extended to online learning scenario, where some newly detected and verified layout patterns are integrated into the learning model. Experimental results show that the proposed batch detection model outperforms all state-of-the-art methods with 3.47% of accuracy improvement and 58.88% of ODST reduction on ICCAD-2012 contest benchmark suite. More importantly, equipped with online learning, our framework can further improve both accuracy and ODST.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
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.
Journal 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 that uses feature tensor generation to extract representative layout features that fit well with convolutional neural networks while keeping the spatial relationship of the original layout pattern with minimal information loss.
Proceedings ArticleDOI
GAN-OPC: mask optimization with lithography-guided generative adversarial nets
TL;DR: A generative adversarial network (GAN) model is developed that can create quasi-optimal masks for given target circuit patterns and fewer normal OPC steps are required to generate high quality masks.
Journal ArticleDOI
A short-term energy prediction system based on edge computing for smart city
TL;DR: A short-term energy prediction system based on edge computing architecture is proposed, in which data acquisition, data processing and regression prediction are distributed in sensing nodes, routing nodes and central server respectively, and an online deep neural network model adapted to the characteristics of IoT data is implemented.
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