A Mixed-Variable Bayesian Optimization Approach for Analog Circuit Synthesis

Bayesian Optimization (BO) is an efficient method for black-box optimization problems. It has been successfully applied to the analog circuit sizing problem. However, all the design variables are viewed as continuous variables in these methods. Actually, many design variables are discrete due to the design rules. In this paper, we proposed an improved BO method for analog circuit sizing with both discrete and continuous variables. We also utilize the Gaussian Process (GP) regression model as the surrogate model for BO. However, we modified the kernel of GP and make it applicable to mixed variables. Experimental results demonstrated that the proposed mixed-variable BO method can significantly reduce the number of simulations with comparable optimization results, compared with the existing BO methods.