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Distributionally Robust Circuit Design Optimization under Variation Shifts

https://doi.org/10.1109/ICCAD57390.2023.10323948

Pan, Yifan ; He, Zichang ; Guo, Nanlin ; Zhang, Zheng ( October 2023 , IEEE)

Free, publicly-accessible full text available October 28, 2024
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design

https://doi.org/10.1109/DAC56929.2023.10247998

Liu, Ziyue ; Li, Yixing ; Hu, Jing ; Yu, Xinling ; Shiau, Shinyu ; Ai, Xin ; Zeng, Zhiyu ; Zhang, Zheng ( July 2023 , IEEE)

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PoBO: A Polynomial Bounding Method for Chance-Constrained Yield-Aware Optimization of Photonic ICs

https://doi.org/10.1109/TCAD.2021.3137358

He, Zichang ; Zhang, Zheng ( December 2021 , IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

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Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs

https://doi.org/10.1109/QCS54837.2021.00007

Lykov, Danylo ; Chen, Angela ; Chen, Huaxuan ; Keipert, Kristopher ; Zhang, Zheng ; Gibbs, Tom ; Alexeev, Yuri ( November 2021 , IEEE/ACM Second International Workshop on Quantum Computing Software (QCS))

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Progress of Tensor-Based High-Dimensional Uncertainty Quantification of Process Variations

https://doi.org/10.1109/ACES53325.2021.00020

He, Zichang ; Zhang, Zheng ( August 2021 , International Applied Computational Electromagnetics Society Symposium (ACES))

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Chance-Constrained and Yield-Aware Optimization of Photonic ICs With Non-Gaussian Correlated Process Variations

https://doi.org/10.1109/TCAD.2020.2968582

Cui, Chunfeng ; Liu, Kaikai ; Zhang, Zheng ( December 2020 , IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)
null (Ed.)
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High-Dimensional Uncertainty Quantification via Active and Rank-Adaptive Tensor Regression

https://doi.org/10.1109/EPEPS48591.2020.9231388

He, Zichang ; Zhang, Zheng ( October 2020 , IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS))
null (Ed.)
Uncertainty quantification based on stochastic spectral methods suffers from the curse of dimensionality. This issue was mitigated recently by low-rank tensor methods. However, there exist two fundamental challenges in low-rank tensor-based uncertainty quantification: how to automatically determine the tensor rank and how to pick the simulation samples. This paper proposes a novel tensor regression method to address these two challenges. Our method uses an 12,p-norm regularization to determine the tensor rank and an estimated Voronoi diagram to pick informative samples for simulation. The proposed framework is verified by a 19-dim phonics bandpass filter and a 57-dim CMOS ring oscillator, capturing the high-dimensional uncertainty well with only 90 and 290 samples respectively.
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Prediction of Multidimensional Spatial Variation Data via Bayesian Tensor Completion

https://doi.org/10.1109/TCAD.2019.2891987

Luan, Jiali ; Zhang, Zheng ( February 2020 , IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

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Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models

https://doi.org/10.1109/ICCAD45719.2019.8942121

Cui, Chunfeng ; Hawkins, Cole ; Zhang, Zheng ( November 2019 , International Conference on Computer-Aided Design)

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Stochastic collocation with non-Gaussian correlated process variations: Theory, algorithms, and applications

Cui, Chunfeng ; Zhang, Zheng ( July 2019 , IEEE transactions on components packaging and manufacturing technology)

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