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

点击翻译按钮获取中文摘要

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.