GAN-OPC: Mask Optimization with Lithography-guided Generative Adversarial Nets

Mask optimization has been a critical problem in the VLSI design flow due to the mismatch between the lithography system and the continuously shrinking feature sizes. Optical proximity correction (OPC) is one of the prevailing resolution enhancement techniques (RETs) that can significantly improve mask printability. However, in advanced technology nodes, the mask optimization process consumes more and more computational resources. In this paper, we develop a generative adversarial network (GAN) model to achieve better mask optimization performance. We first develop an OPC-oriented GAN flow that can learn target-mask mapping from the improved architecture and objectives, which leads to satisfactory mask optimization results. To facilitate the training process and ensure better convergence, we also propose a pre-training procedure that jointly trains the neural network with inverse lithography technique (ILT). At convergence, the generative network is able to create quasi-optimal masks for given target circuit patterns and fewer normal OPC steps are required to generate high quality masks. Experimental results show that our flow can facilitate the mask optimization process as well as ensure a better printability.

[1]  A. Zakhor,et al.  Optical Proximity Correction With Linear Regression , 2008, IEEE Transactions on Semiconductor Manufacturing.

[2]  David Z. Pan,et al.  Design for Manufacturing With Emerging Nanolithography , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[3]  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).

[4]  H. H. Hopkins,et al.  The concept of partial coherence in optics , 1951, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[5]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[7]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[8]  David Z. Pan,et al.  A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction , 2015, Advanced Lithography.

[9]  Seongbo Shim,et al.  Machine learning (ML)-guided OPC using basis functions of polar Fourier transform , 2016, Advanced Lithography.

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

[11]  Evangeline F. Y. Young,et al.  A robust approach for process variation aware mask optimization , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[12]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[13]  Yu-Hsuan Su,et al.  Fast Lithographic Mask Optimization Considering Process Variation , 2014, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[14]  Ru-Gun Liu,et al.  Two threshold resist models for optical proximity correction , 2004, SPIE Advanced Lithography.

[15]  Avideh Zakhor,et al.  Fast optical and process proximity correction algorithms for integrated circuit manufacturing , 1998 .

[16]  David Z. Pan,et al.  MOSAIC: Mask optimizing solution with process window aware inverse correction , 2014, 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC).

[17]  Jürgen Schmidhuber,et al.  Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[18]  Sani R. Nassif,et al.  ICCAD-2013 CAD contest in mask optimization and benchmark suite , 2013, 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[19]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

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

[21]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  David Z. Pan,et al.  Process variation aware OPC with variational lithography modeling , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[23]  David Z. Pan,et al.  True process variation aware optical proximity correction with variational lithography modeling and model calibration , 2007 .

[24]  Amyn Poonawala,et al.  Mask Design for Optical Microlithography—An Inverse Imaging Problem , 2007, IEEE Transactions on Image Processing.

[25]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Chikaaki Kodama,et al.  A Machine Learning Based Framework for Sub-Resolution Assist Feature Generation , 2016, ISPD.

[27]  Rui Luo,et al.  Optical proximity correction using a multilayer perceptron neural network , 2013 .

[28]  Satoshi Tanaka,et al.  A Fast Process-Variation-Aware Mask Optimization Algorithm With a Novel Intensity Modeling , 2017, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[29]  David Z. Pan,et al.  Design for manufacturability and reliability in extreme-scaling VLSI , 2016, Science China Information Sciences.

[30]  Wei Xiong,et al.  A Gradient-Based Inverse Lithography Technology for Double-Dipole Lithography , 2009, 2009 International Conference on Simulation of Semiconductor Processes and Devices.

[31]  Jhih-Rong Gao,et al.  A unified framework for simultaneous layout decomposition and mask optimization , 2017, 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[32]  Ahmed Awad,et al.  A fast process variation and pattern fidelity aware mask optimization algorithm , 2014, 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[33]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[34]  Ya-Chieh Lai,et al.  Detecting multi-layer layout hotspots with adaptive squish patterns , 2019, ASP-DAC.

[35]  Ramya Viswanathan,et al.  Process optimization through model based SRAF printing prediction , 2012, Advanced Lithography.

[36]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[37]  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).