SHF:Small: Machine Learning Approach for Fast Electromigration Analysis and Full-Chip Assessment

SHF:Small：用于快速电迁移分析和全芯片评估的机器学习方法

• 批准号: 2007135
• 负责人: Sheldon Tan
• 金额: $ 50万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007135&HistoricalAwards=false
• 关键词: SHF; Small; Machine; Learning; Approach

Electromigration (EM) has become one of the most critical design issues and limiting factors for nanometer VLSI designs because of the shrinking size and increasing power density of the interconnects as technology scales down to sub 5nm. Due to its importance, many advances have been made recently in EM modeling and assessment techniques. However, fast and full-chip level EM analysis and validation still remain a challenging problem as completely modeling the EM failure process requires solving partial differential equations of hydrostatic stress in large interconnects. This will become even more difficult for full-chip level EM sign-off analysis. At the same time, machine learning, especially deep learning based on deep neural networks (DNN) such as convolutional neural networks (CNN), generative adversarial networks (GAN) and auto-encoders, is gaining much attention due to transformative successes in the many cognitive tasks. How to apply deep-learning techniques to learn and encode laws of physics and help to solve nonlinear partial differential equations, however, still remains in its infancy. The new EM optimization techniques will enhance the integrated-circuit (IC) design industry’s ability to improve VLSI long-term reliability amid continued aggressive transistor scaling and increasing power density. This research will also contribute significantly to the core knowledge and technologies of machine learning and data-driven based nonlinear dynamic-system modeling and advanced numerical approaches. This award will enable the investigator to hire more female and underrepresented minority students to further contribute to the diversity in America’s science and technology workforce.This project will explore novel and transformative EM modeling and full-chip EM-induced lifetime assessment techniques based on data-driven deep learning and advanced numerical methods. First, the research will investigate and design new deep-learning-based techniques for transient hydrostatic stress analysis for multi-segment interconnect trees. The project will explore DNN network structures such as CNN, GAN, autoencoders, and physics-informed neural networks for both void nucleation and post-voiding phases of EM failure processes in both circuit and full-chip levels. Second, the project will develop fast analytic and semi-analytic solutions for the stress-based partial differential equations for general multi-segment interconnects considering Joule-heating and thermal-migration effects. At the full-chip level, a coupled multi-physics analysis for fast EM sign-off check of on-chip power ground networks will be investigated.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.

随着工艺尺寸缩小到5nm以下，互连线的尺寸不断缩小，功率密度不断增加，因此电迁移（EM）已成为纳米VLSI设计中最关键的设计问题和限制因素之一。由于其重要性，EM建模和评估技术最近取得了许多进展。然而，快速和全芯片级EM分析和验证仍然是一个具有挑战性的问题，因为完全建模EM故障过程需要求解大型互连中的静水应力的偏微分方程。这对于全芯片级EM签准分析将变得更加困难。 与此同时，机器学习，特别是基于深度神经网络（DNN）的深度学习，如卷积神经网络（CNN），生成对抗网络（GAN）和自动编码器，由于在许多认知任务中取得了变革性的成功，正受到越来越多的关注。然而，如何应用深度学习技术来学习和编码物理定律，并帮助解决非线性偏微分方程，仍处于起步阶段。新的EM优化技术将增强集成电路（IC）设计行业的能力，以提高VLSI的长期可靠性，在持续积极的晶体管规模和增加功率密度。 这项研究还将为机器学习和基于数据驱动的非线性动力系统建模和高级数值方法的核心知识和技术做出重大贡献。该项目将探索基于数据驱动的深度学习和先进数值方法的新颖和变革性EM建模和全芯片EM诱导寿命评估技术。首先，该研究将调查和设计新的基于深度学习的技术，用于多段互连树的瞬态静水压力分析。该项目将探索DNN网络结构，如CNN，GAN，自动编码器和物理信息神经网络，用于电路和全芯片级EM故障过程的空洞成核和空洞后阶段。其次，该项目将开发快速的解析和半解析解的应力为基础的偏微分方程的一般多段互连考虑焦耳加热和热迁移效应。在全芯片级，将研究用于片上电源接地网络的快速EM签核检查的耦合多物理场分析。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

GridNetOpt: Fast Full-Chip EM-Aware Power Grid Optimization Accelerated by Deep Neural Networks

• DOI: 10.1109/tcad.2022.3206397
• 发表时间: 2023-05
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Han Zhou;Yibo Liu;Wentian Jin;S. Tan

Robust power grid network design considering EM aging effects for multi-segment wires

• DOI: 10.1016/j.vlsi.2020.10.001
• 发表时间: 2021-03
• 期刊: Integr.
• 作者: Han Zhou;Liang Chen;S. Tan

HierPINN-EM: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnects Using Hierarchical Physics-Informed Neural Network

HierPINN-EM：使用分层物理信息神经网络对多段互连进行基于快速学习的电迁移分析

• DOI: 10.1145/3508352.3549371
• 发表时间: 2022
• 期刊: Proc. IEEE/ACM International Conf. on Computer-Aided Design (ICCAD’22
• 作者: Jin, Wentian;Chen, Liang;Lamichhane, Subed;Kavousi, Mohammadamir;Tan, Sheldon X.-D.
• 通讯作者: Tan, Sheldon X.-D.

Runtime Long-Term Reliability Management Using Stochastic Computing in Deep Neural Networks

在深度神经网络中使用随机计算的运行时长期可靠性管理

• 发表时间: 2021
• 期刊: Proc. Int. Symposium. on Quality Electronic Design (ISQED’21
• 作者: Liu, Y.;Yu, S.;Peng, S.;Tan, S. X.-D.
• 通讯作者: Tan, S. X.-D.

Fast Physics-Based Electromigration Analysis for Full-Chip Networks by Efficient Eigenfunction-Based Solution

• DOI: 10.1109/tcad.2020.3001264
• 发表时间: 2021-03
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Xiaoyi Wang;Shaobin Ma;Chase Cook;Liang Chen;Jianlei Yang;Wenjian Yu