Uncertainty-Aware Performance Evaluation of Low-Noise Amplers via Generalized Polynomial Chaos Expansion-Based Surrogate Model

Abstract

Statistical performance analysis of low-noise amplifiers (LNA) under process, voltage, and temperature (PVT) variations and device mismatches is crucial for ensuring design robustness. Unfortunately, traditional Monte Carlo (MC) analysis is often computationally prohibitive for complex R F circuits. In this work, a generalized polynomial chaos expansion (gPCE) model is constructed as a surrogate model from a limited set of circuit simulations to approximate a comprehensive figure-of-merit (FoM). The results demonstrate that the third-order gPCE model achieves high predictive accuracy, confirmed by a high coefficient of determination (R2=0. 9 2 5) and a low root mean squared error (RMSE =0.007). In addition, the proposed model enables the extraction of these statistical moments and tail-risk metrics over 26 times faster than the benchmark MC simulation.