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.

电迁移 (EM) 已成为纳米 VLSI 设计最关键的设计问题和限制因素之一，因为随着技术缩小到 5nm 以下，互连的尺寸不断缩小，功率密度不断增加。由于其重要性，已经取得了许多进展。然而，快速、全芯片级的 EM 分析和验证仍然是一个具有挑战性的问题，因为对 EM 失效过程进行完全建模需要求解大型互连中的静水应力偏微分方程。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）和自动生成网络（GAN）。编码器由于在许多认知任务中取得的变革性成功而受到广泛关注，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然处于起步阶段。 EM 优化技术将增强集成电路 (IC) 设计行业在晶体管尺寸不断缩小和功率密度不断增加的情况下提高 VLSI 长期可靠性的能力。这项研究还将为机器学习和数据的核心知识和技术做出重大贡献。基于驱动的非线性动态系统建模和先进的数值方法将使研究人员能够雇用更多的女性和代表性不足的少数族裔学生，进一步为美国科技劳动力的多样性做出贡献。该项目将探索新颖且变革性的电磁建模和全面的研究。 -芯片首先，该研究将研究和设计基于深度学习的新技术，用于多段互连树的瞬态静水应力分析。其次，该项目将开发快速分析和分析的网络结构，例如 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别的 EM 故障过程的空洞成核和空洞后阶段。考虑焦耳热和热迁移效应的通用多段互连的基于应力的偏微分方程的半解析解在全芯片级别上，用于快速电磁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

Run-time accuracy reconfigurable stochastic computing for dynamic reliability and power management: work-in-progress

用于动态可靠性和电源管理的运行时精度可重构随机计算：正在进行中

• 发表时间: 2020-10
• 期刊: ESWEEK 2020
• 作者: Yu, S.;Zhou, H.;Amrouch, H.;Henkel, J.;Tan, S. X.
• 通讯作者: Tan, S. X.

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: 2022-10
• 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.
• 通讯作者: Tan, Sheldon X.

Hot-Trim: Thermal and Reliability Management for Commercial Multi-core Processors Considering Workload Dependent Hot Spots

Hot-Trim：考虑工作负载相关热点的商用多核处理器的热和可靠性管理

• DOI: 10.1109/tcad.2022.3216552
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Zhang, Jinwei;Sadiqbatcha, Sheriff;Tan, Sheldon X.
• 通讯作者: Tan, Sheldon X.

Fast electromigration stress analysis considering spatial Joule heating effects

考虑空间焦耳热效应的快速电迁移应力分析

• 发表时间: 2022-01
• 期刊: Proc. Asia South Pacific Design Automation Conference (ASP-DAC’22
• 作者: Kavousi, M.;Chen, L.;Tan, S.
• 通讯作者: Tan, S.

EM-GAN: Data-driven fast stress analysis for multi-segment interconnects

EM-GAN：数据驱动的多段互连快速应力分析

• 发表时间: 2020-10
• 期刊: Proc. IEEE Int. Conf. on Computer Design (ICCD
• 作者: Jin, W.;Sadiqbatcha, S.;Sun, Z.;Zhou, H.;Tan, S. X.
• 通讯作者: Tan, S. X.