Gaussian-process-based surrogate for optimization-aided and process-variations-aware analog circuit design

Abstract

Optimization algorithms have been successfully applied to the automatic design of analog integrated circuits. However, many of the existing solutions rely on expensive circuit simulations or use fully customized surrogate models for each particular circuit and technology. Therefore, the development of an easily adaptable low-cost and efficient tool that guarantees resiliency to variations of the resulting design, remains an open research area. In this work, we propose a computationally low-cost surrogate model for multi-objective optimization-based automated analog integrated circuit (IC) design. The surrogate has three main components: a set of Gaussian process regression models of the technology’s parameters, a physics-based model of the MOSFET device, and a set of equations of the performance metrics of the circuit under design. The surrogate model is inserted into two different state-of-the-art optimization algorithms to prove its flexibility. The efficacy of our surrogate is demonstrated through simulation validation across process corners in three different CMOS technologies, using three representative circuit building-blocks that are commonly encountered in mainstream analog/RF ICs. The proposed surrogate is 69X to 470X faster at evaluation compared with circuit simulations.