CAREER: Uncertainty-Aware and Data-Driven Methods for Electronic and Photonic Design Automation

职业：电子和光子设计自动化的不确定性感知和数据驱动方法

• 批准号: 1846476
• 负责人: Zheng Zhang
• 金额: $ 51.03万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846476&HistoricalAwards=false
• 关键词: CAREER; Uncertainty; Aware; Data; Driven

Designing complex engineering systems such as self-driving cars, electronic and photonic integrated circuits, requires design automation software to complete many challenging tasks that are impossible or too time-consuming if done manually. In practice, almost all engineering designs are subject to such unavoidable uncertainties as noise, fabrication process variations and insufficient knowledge about external environments. These uncertainties often cause performance degradations, system failures and sometimes fatal accidents. However, existing design automation software requires massive data samples from time-consuming computer simulations or non-trivial measurement when uncertainties are involved. This project uses electronic and photonic integrated circuits as driving examples, and will develop novel design automation algorithms to improve the performance and reliability under various uncertainties. The education components of this project include creating two graduate courses of uncertainty and data analysis, training future workforce through undergraduate and graduate research. The outreach education and training through the awardee institution and through academic conferences will enable technology and knowledge transfer to a broad community. Although this project targets on applications in electronics and photonics, the developed algorithms and theory will be applicable to many other domains such as autonomous driving, renewable energy systems, and medical imaging. Since uncertainty-aware photonic design automation is still at its early stage, this project will enable a new field of important research. The resulting algorithms and tools will support the foreseeable large-scale photonic integration which will boost the performance of future computing and communication systems. The technical goal of this project is to develop novel uncertainty-aware electronic and photonic design automation algorithms that require only a small data set and a very low computational cost in the design flow. This project will span three research topics: uncertainty-aware simulation, optimization and data-driven variation modeling. Firstly, novel algorithms will be developed to address several long-standing challenges in the forward uncertainty quantification of electronic and photonic circuits, such as the coupled impact of fundamentally different types of uncertainties and long-term probabilistic simulation errors. Secondly, leveraging the developed forward uncertainty simulator, this project will further develop ultra-fast optimization tools to improve the yield of electronic and photonic circuits. The main focus will be investigating large-scale "non-sampling" stochastic optimization algorithms. The developed algorithms will enable rigorous yield optimization with "small" simulation data sets and thus significantly reduce the software runtime on a computer. Finally, rigorous statistical estimation algorithms will be developed to calibrate critical device model parameters and to extract statistical variability distributions based on limited and noisy indirect circuit-level measurement data. The designed algorithms and prototyping software will be validated by practical design cases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

设计复杂的工程系统，例如自动驾驶汽车、电子和光子集成电路，需要设计自动化软件来完成许多具有挑战性的任务，如果手动完成这些任务是不可能或太耗时的。在实践中，几乎所有的工程设计都受到不可避免的不确定性的影响，例如噪声、制造工艺变化以及对外部环境的了解不足。这些不确定性通常会导致性能下降、系统故障，有时甚至导致致命事故。然而，现有的设计自动化软件需要来自耗时的计算机模拟或涉及不确定性的重要测量的大量数据样本。该项目以电子和光子集成电路为驱动示例，将开发新颖的设计自动化算法，以提高各种不确定性下的性能和可靠性。该项目的教育部分包括创建两门不确定性和数据分析研究生课程，通过本科生和研究生研究培训未来的劳动力。通过获奖机构和学术会议进行的外展教育和培训将使技术和知识向更广泛的社区转移。尽管该项目的目标是电子和光子学领域的应用，但所开发的算法和理论将适用于自动驾驶、可再生能源系统和医学成像等许多其他领域。由于不确定性感知光子设计自动化仍处于早期阶段，该项目将开启一个新的重要研究领域。由此产生的算法和工具将支持可预见的大规模光子集成，从而提高未来计算和通信系统的性能。 该项目的技术目标是开发新颖的不确定性感知电子和光子设计自动化算法，该算法在设计流程中仅需要较小的数据集和非常低的计算成本。 该项目将涵盖三个研究主题：不确定性感知模拟、优化和数据驱动的变化建模。首先，将开发新的算法来解决电子和光子电路的前向不确定性量化中的几个长期挑战，例如根本不同类型的不确定性和长期概率模拟误差的耦合影响。其次，利用已开发的前向不确定性模拟器，该项目将进一步开发超快速优化工具，以提高电子和光子电路的良率。主要重点将是研究大规模“非采样”随机优化算法。开发的算法将通过“小”模拟数据集实现严格的良率优化，从而显着减少计算机上的软件运行时间。最后，将开发严格的统计估计算法来校准关键器件模型参数，并根据有限且嘈杂的间接电路级测量数据提取统计变异性分布。设计的算法和原型软件将通过实际设计案例进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Distributionally Robust Circuit Design Optimization under Variation Shifts

• DOI: 10.1109/iccad57390.2023.10323948
• 发表时间: 2023-08
• 期刊: 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)
• 作者: Yifan Pan;Zichang He;Nanlin Guo;Zheng Zhang

Recent Advancements of Uncertainty Quantification with Non-Gaussian Correlated Process Variations: Invited Special Session Paper

非高斯相关过程变化的不确定性量化的最新进展：特邀特别会议论文

• DOI: 10.1109/nemo.2019.8853732
• 发表时间: 2019
• 期刊: IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO
• 作者: Cui, Chunfeng;Zhang, Zheng
• 通讯作者: Zhang, Zheng

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

• DOI: 10.1109/qcs54837.2021.00007
• 发表时间: 2021-11
• 期刊: 2021 IEEE/ACM Second International Workshop on Quantum Computing Software (QCS)
• 作者: Danylo Lykov;Angela Chen;Huaxuan Chen;Kristopher Keipert;Zheng Zhang;Tom Gibbs;Y. Alexeev

Prediction of Multidimensional Spatial Variation Data via Bayesian Tensor Completion

• DOI: 10.1109/tcad.2019.2891987
• 发表时间: 2019-01
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Jiali Luan;Zheng Zhang

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

• DOI: 10.1109/tcad.2020.2968582
• 发表时间: 2019-08
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Chunfeng Cui;Kaikai Liu;Zheng Zhang