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

近年来，微系统变得无处不在，复杂且越来越便宜，电子设计自动化工具的可用性是这个数十亿美元行业成功的必要先决条件。与此同时，这类系统的复杂性呈指数增长。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，微系统通常包括微机电系统（MEMS），各种传感器和光学互连，以形成复杂的多物理场微系统。同时，这种复杂性和相应的性能提高是在不增加成本的情况下实现的。事实上，虽然最先进的cpu的成本仍然很高，但现在有可能以几年前的一小部分成本获得更简单但仍然相对强大的计算机。这个行业的一些关键推动者是电子设计自动化（EDA）工具。这样的工具允许系统化的设计流程，以及在遵从日益快速的产品更新周期的同时设计和管理高度复杂的系统的能力。在这个研究项目中，我们的目标是从仿真和设计自动化的角度来解决与高性能芯片、封装和系统的信号完整性和功率完整性相关的关键战略问题。同时，我们将通过开发易于并行计算的算法来考虑现代多核计算架构。更具体地说，我们将重点关注以下一般主题：1。模型阶数约简（MOR）：模型约简背后的一般思想是，具有许多自由度（可达数百万）的大型非常复杂的系统通常具有相对较少的主导模式。我们的目标是开发更多的算法，将大问题减少到更小的问题，这样它们就可以有效地模拟，同时保持可接受的精度。这项工作的一个重要焦点是开发技术，将一个大系统减少到许多可以并行解决的小系统，从而实现并行计算的使用。2. 基于测量和模拟参数的黑盒建模：在我们的研究中，我们将重点关注自动模型生成。对于许多实际应用来说，建立基于物理的数学模型是非常困难的。我们将开发工具和算法，自动获得基于测量或模拟参数的电源和信号互连结构的数学模型。3. 机器学习方法论：近年来，机器学习领域取得了相当大的进展。这一领域与基于神经网络的建模和复杂电气系统的替代建模有很多共同之处。我们计划开发受机器学习启发的算法和方法，用于信号和电源完整性仿真和优化。

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.