FlowSim: An Invertible Generative Network for Efficient Statistical Analysis under Process Variations

Hong Chul Nam, Chanwoo Park, JihunPark, Jongwook Jeon

Abstract: The analysis of statistical variation in circuits or devices, resulting from process, voltage, and temperature (PVT) variations, is a critical aspect of ensuring high yield and accurate high-sigma analysis in semiconductor fabrication. As the industry progresses toward nanometer technologies, process variation becomes a significant challenge, necessitating the development of effective statistical models. Traditional Monte Carlo simulations, however, struggle to scale with the increasing number of process variables, leading to an exponential growth in the required simulations. In response to this challenge, we introduce FlowSim, a novel approach that employs density estimation to accurately perform yield and high-sigma analysis with a significantly reduced number of simulations. This approach offers a unique solution to the scalability issues faced by conventional Monte Carlo simulations, providing over a 100x decrease in the number of required simulations while maintaining a prediction error below 5% across all statistical metrics of circuit performance.