Efficient Global Optimization of Analog Circuits Using Predictive Response Surface Models on Discretized Design Space

Abstract

Abstract─ An efficient global optimization method is presented. Typically, simulation-based optimizers have long execution time due to the large number of simulations required during the optimization process.This paper tries to reduce the simulation number with two existing techniques: first, discretizing the continuous design space with respect to the variability-induced correlation, making total number of design candidate to be finite; second, performing simulation for only the best design candidates determined based on response surface models (RSMs) on stochastic circuit performance. For more efficient searching, extrapolative RSM and a heuristic for finding best candidate are also introduced. The RSM-based searching is demonstrated to significantly enhance the scalability of discrete, gradient-based searching. Also, it shows 2x~3x reduction in the number of simulations for the optimization of a standard two-stage operational amplifier, a voltage-controlled LC oscillator and a differential voltage-controlled ring oscillator compared to the genetic algorithm.

A Comprehensive Review on Automation-based Sizing Techniques for Analog IC Design

“…Yield optimization needs to be considered in the design stage to allow finding solutions with low sensitivity to process, voltage and temperature variations. Several works in the literature propose techniques to estimate and optimize yield in analog circuits [56,57,58,13,59,7]. Some of these works divide the design into two phases.…”