SHF:Small: Learning-based Fast Analysis and Fixing for Electromigration Damage

SHF:Small：基于学习的电迁移损伤快速分析和修复

• 批准号: 2305437
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2305437&HistoricalAwards=false
• 关键词: SHF; Small; Learning; based; Fast

Electromigration (EM) has a significant reliability issue and limiting factor for advanced very large scale integration (VLSI) designs due to the shrinking size and increasing current density of copper-based interconnects in sub-3nm technology. As a result, future chips are expected to age faster than previous generations. While recent advances in EM modeling and assessment techniques have been made, fast and accurate EM analysis and automatic optimization for large-scale power grid networks remain challenging due to the need for physics-based modeling that involves solving partial differential equations for hydrostatic stress in large interconnects. This becomes even more difficult for full-chip level EM management. Machine learning techniques, particularly deep learning based on deep neural networks (DNNs), such as convolutional neural networks (CNNs), and scientific machine learning (SciML) approaches, have emerged as promising solutions to traditional numerical analysis techniques for solving partial differential equations (PDEs). The unsupervised physics-informed/constrained neural network (PINN/PCNN) framework in the SciML field shows powerful capabilities such as mesh-free and parametrized numerical solutions. However, existing PINN/PCNN works can only solve small PDE problems with simple boundary conditions. For large engineering problems with millions of variables commonly seen in design automation, PINN/PCNN approaches show slow convergence, if they converge at all. Additionally, fixing EM-induced failure or damage to achieve the expected EM mean time to failure at both design and run times remains challenging due to the sensitivity-based optimization framework and lack of sufficient on-chip temperature sensors. The tools to be developed in this project will be valuable to advancing the understanding of this important problem and curtailing the lifetime reliability issue of complementary metal-oxide semiconductor (CMOS) chips.This project will develop novel learning-based EM analysis based on PINN/PCNN framework and efficient machine learning-accelerated full-chip EM fixing and run-time management methods for VLSI chips in the nanometer regime. First, the project will explore new SciML-based solutions such as enhanced PINN/PCNN methods, for hydrostatic stress analysis for multi-segment interconnect trees. On top of those methods, full-chip multi-physics coupled EM induced IR drop (i.e., reduction in voltage) analysis and lifetime estimation for power grid networks considering Joule heating effects will be developed. Second, the project will develop efficient full-chip EM-aware power grid optimization techniques, aided by DNNs, along with a dynamic run-time EM-aware management method to identify the real hotspots of processors. The project will investigate DNN-accelerated full-chip power grid optimization by exploiting the differentiability of the trained DNN models to allow for rapid sensitivity calculations based on a sequence of linear programming techniques. Finally, we will devise a DNN-based method to estimate hotspots and develop a dynamic run-time EM lifetime management method, considering the actual hotspots of processors.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.

电气移民（EM）由于尺寸缩小和低3NM技术中铜互连的电流密度增加而具有高级非常大规模集成（VLSI）设计的重大可靠性问题和限制因素。结果，预计未来的芯片年龄要比前几代快。尽管已经取得了最新的EM建模和评估技术的进步，但由于需要基于物理学的建模，涉及在大型互连中求解偏微分方程的部分差分方程，因此快速准确的EM分析和对大规模电网网络的自动优化仍然具有挑战性。对于全芯片级别的EM管理，这将变得更加困难。机器学习技术，尤其是基于深层神经网络（DNN）的深度学习，例如卷积神经网络（CNN）和科学机器学习（SCIML）方法，已成为用于解决部分微分方程（PDES）的传统数值分析技术的有希望的解决方案。 SCIML字段中的无监督物理信息/约束神经网络（PINN/PCNN）框架显示出强大的功能，例如无网格和参数化的数值解决方案。但是，现有的PINN/PCNN作品只能在简单的边界条件下解决小的PDE问题。对于在设计自动化中常见的数百万变量的大型工程问题，PINN/PCNN方法如果完全收敛，则会显示出缓慢的收敛性。此外，由于基于灵敏度的优化框架和缺乏足够的芯片片上温度传感器，固定EM诱导的故障或损坏以实现预期的EM平均时间在设计和运行时间上的平均时间仍然具有挑战性。该项目中要开发的工具对于促进对这一重要问题的理解并缩小了互补金属氧化物半导体（CMOS）芯片的终身可靠性问题非常有价值。本项目将基于PINN/PCNN框架和有效的机器学习 - 固定和运行时间管理方法来开发基于新学习的EM分析。首先，该项目将探索基于SCIML的新解决方案，例如增强的PINN/PCNN方法，用于用于多段互连树的静水压力分析。除这些方法外，考虑使用焦耳加热效应的电网网络的全芯片多物理耦合EM诱导的IR下降（即电压降低）分析和寿命估计。其次，该项目将开发有效的全芯片EM-感知功率电网优化技术，并在DNNS的帮助下，以及动态的运行时EM-Aware Aranage Management方法，以识别处理器的真实热点。该项目将通过利用训练有素的DNN模型的可不同性来研究DNN加速的全芯片功率电网优化，以允许基于线性编程技术的序列进行快速敏感性计算。最后，我们将设计一种基于DNN的方法来估计热点并开发动态的运行时EM终生管理方法，考虑到处理器的实际热点。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响标准通过评估来评估的。