Analytic Passive Intermodulation Behavior on the Coaxial Connector Using Monte Carlo Approximation

TLDR

The study introduces a novel passive intermodulation prediction method for coaxial connectors that accounts for random contact behavior to analyze fluctuated PIM and inspire new risk prediction approaches. Using a micro‑level smart contact model and Monte Carlo approximation, the authors reconstruct random distributed contact samples for connector components and generate a PIM confidence interval instead of a single prediction. Experimental results show good agreement with measured PIM, demonstrating the method’s effectiveness.

Abstract

This paper presents a novel passive intermodulation (PIM) prediction method considering random contact behavior using a Monte Carlo method for a coaxial connector. A smart contact model for a contact unit at a microcosmic level is proposed. Using Monte Carlo approximation and micromeasurements, different random distributed contact samples for different contact components inside the coaxial connector are reconstructed. In the experiment, PIM on inner and outer conductor was tested and compared with predication. A good agreement proves the proposed PIM prediction method is efficient. Rather than generating a single PIM prediction value, this method will give a PIM confidence interval for all the potential PIM values considering the contact force statistical behavior. The work will help analyze fluctuated PIM on coaxial connectors and inspire a new method to predict PIM risk.