Simulation, Modeling and Design Automation for High-Performance Chip, Package and Systems

近年来，微系统变得无处不在、复杂和 越来越便宜，电子设计自动化的可用性 工具是这笔数十亿美元成功的必要前提 工业。与此同时，这类系统的复杂性也增加了 成倍增长。一个典型的微芯片现在包含数十亿个晶体管和许多 计算核心。此外，微系统通常包括微电子机械 系统(MEMS)、各种传感器和光学互连形成复合体 多物理微系统。同时，这种复杂性和 相应的性能提升在没有显著提高的情况下实现 成本的增加。事实上，尽管最先进的CPU的成本仍然保持在 溢价，现在有可能获得更简单但仍然相对强大的 电脑的价格只有几年前的一小部分。一些关键的推动因素 电子设计自动化(EDA)工具是该行业的重要组成部分。这样的工具允许 系统的设计流程，以及高度的设计和管理能力 复杂的系统，同时遵守越来越快的产品更新周期。 在这个研究计划中，我们旨在解决与以下相关的关键战略问题 高性能芯片的信号完整性和电源完整性，封装和 系统从模拟和设计自动化的角度。在同一时间 我们将通过以下方式考虑现代多核计算体系结构 开发易于并行计算的算法。更多 具体而言，我们将重点关注以下一般性主题： 1.模型降阶(MOR)：模型降阶背后的总体思想是 具有许多自由度的大型非常复杂的系统(范围包括 数百万)通常具有相对较少数量的主导模式。我们的目标 是开发MOR算法，将大问题简化为小问题，如 它们可以在保持可接受的精度的同时被有效地模拟。一个 这项工作的重要重点是开发减少大型系统的技术 可以并行解决的许多较小的系统，从而能够使用 并行计算。 2.基于测量和模拟参数的黑盒建模： 我们的研究重点将集中在自动模型生成上。对许多人来说 实际应用：基于物理的开发是非常困难的 数学 模特们。我们将开发工具和算法来自动获取 基于测量或仿真的电源和信号互连结构的数学模型 参数。 3.机器学习方法论：近年来，有相当多的 机器学习领域的进展。这个地区与……有许多共同之处 基于神经网络的 复杂电气系统的建模与代理建模 系统。我们计划开发受机器学习启发的算法和 信号和电源完整性模拟和优化的方法。

In recent years, Microsystems have become ubiquitous, complex and increasingly inexpensive, and the availability of Electronic Design Automation tools was a necessary prerequisite for the success of this multibillion dollar industry. At the same time, the complexity of such systems has increased exponentially. A typical microchip now contains billions of transistors and many computing cores. Also, microsystems often include microelectromechanical systems (MEMS), various sensors, and optical interconnects to form complex multiphysics microsystems. At the same time, this complexity and corresponding increase in performance is being achieved without considerable increases in cost. Indeed, while the cost of the most advanced CPUs remains at a premium, it is now possible to obtain simpler yet still relatively powerful computers at a fraction of the cost of a few years ago. Some of the key enablers of this industry are Electronic Design Automation (EDA) tools. Such tools allow for systematic design flows, and the ability to design and manage highly complex systems while obeying increasingly fast product update cycles. In this research program, we aim to address critical strategic problems related to signal integrity and power integrity of high performance chip, package and systems from a simulation and design automation perspective. At the same time we will take into account modern multicore computing architectures by developing algorithms that can be readily computed in parallel. More specifically we will focus on the following general themes: 1. Model Order Reduction (MOR): The general idea behind model reduction is that large very complex systems with many degrees of freedoms (ranging into the millions) often have a relatively small number of dominant modes. Our goal is to develop MOR algorithms that reduce large problems into smaller ones, such that they can be efficiently simulated while maintaining acceptable accuracy. An important focus of this work is to develop techniques to reduce a large system into many smaller systems that can be solved in parallel thus enabling the use of parallel computing. 2. Blackbox modeling based on measured and simulated parameters: In this thrust of our research we will focus on automated model generation. For many practical applications it is very difficult to develop physicsbased mathematical models. We will develop tools and algorithms for automatically obtaining mathematical models of power and signal interconnect structures based on measured or simulated parameters. 3. Machine Learning Methodology: In recent years there have been considerable advances in the machine learning field. This area has much in common with neural networksbased modeling and surrogate modeling of complex electrical systems. We plan to develop machine learning inspired algorithms and methodologies for signal and power integrity simulation and optimization.