Signal Integrity Analysis and Design Optimization using Neural Networks

The analysis of high-speed networks is often carried out using transistor level simulation tools which have large computational time. This leads to a limitation in terms of the amount of time spent generating an optimal design and accurately analyzing the system. Therefore, there is a need for fast and accurate modeling of packages and boards, which is the key for developing high performance devices. This paper discusses a machine learning based approach using neural networks to generate a fast model, eliminating the need to run long simulations using EM solvers often. This helps in creating the most optimal design faster without going through many iterations. ML based fast learned model is obtained for a differential PTH. An effective way to generate datasets for training the ML model is discussed. The generated ML model shows a 200X improvement over HFSS while simulating a single design using the Inference model of the neural network.