PASSIOT: A Pareto-optimal multi-objective optimization approach for synthesis of analog circuits using Sobol' indices-based engine

This paper presents a new approach for multi-objective optimization synthesis of analog circuits based on computing Sobol' indices for vectors of input variable parameters ζ of analog circuits, PASSIOT. By adopting Sobol' sensitivity analysis, the tool quantifies the amount of variance that each parameter (or a set of parameters) contributes to the uncertainty of model outputs, which are represented in the objective functions of the circuit. Assessing different higher order sensitivities effects of different sets of input parameters on model outputs contributes to: quantifying parameters having the highest impact on outputs uncertainties, traversing output design space efficiently, and converging to a higher circuit performance in a reasonable runtime compared to other approaches. PASSIOT adopts corner-driven Pareto-Optimal solution model for efficient multi-objective optimization of analog circuits parameters, accounting for on-chip performance variations. Experimental results are simulated on two well-known operational amplifier topologies, the Folded-Cascode amplifier and Miller Compensated Two-Stage amplifier. PASSIOT is simulated using real optimization functions and is proven to be competitive in terms of runtime and solution quality.