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.

由于亚3 nm技术中铜基互连的尺寸缩小和电流密度增加，电迁移（EM）对于先进的超大规模集成（VLSI）设计具有显著的可靠性问题和限制因素。因此，未来的芯片预计会比前几代芯片老化得更快。虽然EM建模和评估技术的最新进展已经取得，快速和准确的EM分析和自动优化的大规模电网网络仍然具有挑战性，由于需要基于物理的建模，涉及求解偏微分方程的静水压力在大型互连。这对于全芯片级EM管理变得更加困难。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，已经成为用于求解偏微分方程（PDE）的传统数值分析技术的有前途的解决方案。SciML领域的无监督物理信息/约束神经网络（PINN/PCNN）框架显示了强大的功能，如无网格和参数化数值解。然而，现有的PINN/PCNN工作只能解决具有简单边界条件的小型PDE问题。对于设计自动化中常见的具有数百万变量的大型工程问题，PINN/PCNN方法即使收敛也会收敛缓慢。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EM引起的故障或损坏以在设计和运行时实现预期的EM平均故障时间仍然具有挑战性。该项目将开发基于PINN/PCNN框架的新的基于学习的EM分析和有效的机器学习加速的全芯片EM修复和运行时管理方法，用于纳米级超大规模集成电路芯片。首先，该项目将探索新的基于SciML的解决方案，如增强的PINN/PCNN方法，用于多段互连树的静水应力分析。在这些方法之上，全芯片多物理场耦合的EM引起的IR降（即，将开发考虑焦耳热效应的电网网络的电压降低）分析和寿命估计。其次，该项目将开发高效的全芯片EM感知电网优化技术，在DNN的帮助下，沿着动态运行时EM感知管理方法，以识别处理器的真实的热点。该项目将研究DNN加速的全芯片电网优化，方法是利用经过训练的DNN模型的可微性，基于一系列线性规划技术进行快速灵敏度计算。最后，我们将设计一个基于DNN的方法来估计热点，并开发一个动态的运行时EM寿命管理方法，考虑到处理器的实际热点。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。