Efficient Uncertainty Quantification for the Periodic Steady State of Forced and Autonomous Circuits

TL;DR

This paper introduces an efficient uncertainty quantification method for periodic steady-state analysis in circuits, outperforming traditional Monte Carlo and spectral methods in speed and accuracy.

Contribution

It presents a novel stochastic testing formulation and a stochastic shooting Newton solver for both forced and autonomous circuits, enhancing computational efficiency.

Findings

Superior efficiency over Monte Carlo and spectral methods

Effective for both Gaussian and non-Gaussian variations

Validated on analog/RF circuits

Abstract

This brief paper proposes an uncertainty quantification method for the periodic steady-state (PSS) analysis with both Gaussian and non-Gaussian variations. Our stochastic testing formulation for the PSS problem provides superior efficiency over both Monte Carlo methods and existing spectral methods. The numerical implementation of a stochastic shooting Newton solver is presented for both forced and autonomous circuits. Simulation results on some analog/RF circuits are reported to show the effectiveness of our proposed algorithms.