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.

由于 3nm 以下技术中铜基互连的尺寸不断缩小和电流密度不断增加，电迁移 (EM) 存在严重的可靠性问题，也是先进超大规模集成 (VLSI) 设计的限制因素。因此，未来的芯片预计会比前几代芯片老化得更快。尽管电磁建模和评估技术取得了最新进展，但由于需要基于物理的建模（涉及求解大型互连中的静水应力偏微分方程），因此大规模电网网络的快速、准确的电磁分析和自动优化仍然具有挑战性。这对于全芯片级 EM 管理来说变得更加困难。机器学习技术，特别是基于深度神经网络 (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，已成为解决偏微分方程 (PDE) 的传统数值分析技术的有前景的解决方案。 SciML 领域的无监督物理信息/约束神经网络 (PINN/PCNN) 框架展现了无网格和参数化数值解等强大功能。然而，现有的 PINN/PCNN 工作只能解决边界条件简单的小型偏微分方程问题。对于设计自动化中常见的具有数百万个变量的大型工程问题，PINN/PCNN 方法即使收敛，也显示出收敛速度较慢。此外，由于基于灵敏度的优化框架和缺乏足够的片上温度传感器，修复电磁引起的故障或损坏以在设计和运行时实现预期的电磁平均故障时间仍然具有挑战性。该项目将开发的工具对于增进对这一重要问题的理解并减少互补金属氧化物半导体（CMOS）芯片的寿命可靠性问题非常有价值。该项目将开发基于PINN/PCNN框架的新颖的基于学习的EM分析，以及用于纳米级VLSI芯片的高效机器学习加速的全芯片EM修复和运行时管理方法。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，用于多段互连树的静水应力分析。除了这些方法之外，还将开发考虑焦耳热效应的全芯片多物理场耦合电磁感应电压降（即电压降低）分析和电网网络寿命估计。其次，该项目将在 DNN 的辅助下开发高效的全芯片 EM 感知电网优化技术，以及动态运行时 EM 感知管理方法来识别处理器的真正热点。该项目将通过利用经过训练的 DNN 模型的可微性来研究 DNN 加速的全芯片电网优化，以允许基于一系列线性编程技术进行快速灵敏度计算。最后，我们将设计一种基于 DNN 的方法来估计热点，并考虑处理器的实际热点，开发动态运行时 EM 生命周期管理方法。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，认为值得支持。