Broad-band poly-harmonic distortion (PHD) behavioral models from fast automated simulations and large-signal vectorial network measurements

TLDR

The paper proposes an optimal experiment design and fully automated model generation method for identifying broad‑band multiharmonic behavioral models in the frequency domain. The method reduces nonlinear measurements, shortens data generation and model extraction times, exploits specialized simulation algorithms, has been validated on real microwave ICs, and is implemented in Agilent’s Advanced Design System. The resulting model accurately predicts harmonics, load‑pull behavior, adjacent‑channel power ratio, and constellation diagrams for both small and large drive signals, matching the underlying circuit model.

Abstract

We present an optimal experiment design methodology and a superior and fully automated model generation procedure for identifying a class of broad-band multiharmonic behavioral models in the frequency domain. The approach reduces the number of nonlinear measurements needed, minimizes the time to generate the data from simulations, reduces the time to extract the model functions from data, and when used for simulation-based models, takes maximum advantage of specialized simulation algorithms. The models have been subject to extensive validation in applications to real microwave integrated circuits. The derived model is valid for both small and large amplitude drive signals, correctly predicts even and odd harmonics through cascaded chains of functional blocks, simulates accurately load-pull behavior away from 50 /spl Omega/, and predicts adjacent channel power ratio and constellation diagrams in remarkably close agreement to the circuit model from which the behavioral model was derived. The model and excitation design templates for generating them from simulations are implemented in Agilent Technologies' Advanced Design System.