Modeling Nonlinear Black-Box Conducted Immunity of Mixed Analog-Digital Integrated Circuits Using Particle Swarm Optimization (PSO) and Piecewise Volterra Series

Abstract

This article addresses the challenge of modeling the conducted immunity of mixed analog-digital integrated Circuits under electromagnetic interference (EMI). We propose a black-box modeling method, integrated circuits for RF immunity behavioral simulation—conducted immunity modeling using particle swarm optimization and piecewise Volterra series [ICIM-CI(PSVIB)]. This method leverages particle swarm optimization (PSO) and piecewise Volterra Series to enhance the immunity behavior module of the ICIM-CI model, aimed at simulating RF immunity in integrated circuits for conducted immunity scenarios. The proposed model accurately describes the nonlinear behavior of integrated circuits using the piecewise Volterra series and significantly improves model accuracy and generality by optimizing the segmentation threshold with the PSO algorithm. This approach overcomes the limitations of traditional ICIM-CI models, which assume linearity and thus struggle to precisely capture the nonlinear response of mixed analog-digital integrated circuits under EMI. Additionally, the proposed model addresses deficiencies in quantitative sensitivity analysis, output of quantitative information, and parametric cascade simulation. Experimental results demonstrate that the ICIM-CI (PSVIB) model provides accurate quantitative sensitivity analysis, outputs comprehensive quantitative information, supports parametric cascade simulation, and exhibits high generality. Compared to the traditional ICIM-CI model, the normalized mean square error of broadband modeling improves by at least 7.3 dB.