Selecting the right EMI/EMC filter is a major challenge when system noise levels exceed compliance or pre-compliance limits. Inline PCB filters are designed to mitigate noise in standalone conditions, but their behavior changes when integrated into a larger system due to unknown parasitic’s. These parasitic’s can disrupt electromagnetic compatibility (EMC), leading to non-compliance [1, 2]. To address this, engineers often use off-the-shelf EMI filters, but determining their real-world effectiveness remains complex. Even with simulation-based methods, accurately predicting insertion loss and attenuation is difficult due to limitations in conventional modeling approaches [4, 5].
Traditional SPICE-based simulations rely on static models defined at specific frequency points, with interpolated values for intermediate frequencies. This interpolation introduces inaccuracies, affecting the precision of simulated results [6, 8]. To overcome these limitations, we propose a methodology that reconstructs a realistic EMI/EMC filter model based on insertion loss characteristics under symmetrical and unsymmetrical conditions.
Our approach involves deconstructing the EMI/EMC filter into its subcomponents—X-capacitance, Y-capacitance, common mode choke CMC, busbar, and PCB traces—and parameterizing them using CST simulations [6, 8]. Instead of relying on physical measurements, which are prone to parasitic influences, we extract subcomponent values from datasheets. We focus on dominant parasitic elements exceeding 1 pF and 1 nH, as lower values predominantly affect GHz-range frequencies rather than the MHz-range compliance limits [4, 5], analyzing impact of parasitic’s on insertion loss, resonance, and damping characteristics, a filter model that represents real-world behavior with a certain error percentage [2, 4, 5].
This methodology results in a high-fidelity EMI/EMC filter model with minimal deviation from actual performance. It enables precise pre-compliance conducted emissions simulations and facilitates optimized filter selection and tuning based on system-specific noise conditions.