An estimation method of magnetic coupling coefficient between two microstrip lines using machine leaning of near field information

An estimation method of magnetic coupling coefficients between printed-circuit-board-level traces using a near field information was investigated. Parallel two microstrip lines (MSLs) with different distances between the lines were used as a test bench. The current flowing in a signal line and its return current was modeled as a simple one-turn equivalent loop current model. First, a one-dimensional convolutional neural network (CNN) for regression prediction was trained with the theoretical values of the magnetic near-field distribution generated from the loop current model. Next, the simulated magnetic near-field distributions above the parallel two MSLs at 1 GHz were input to the trained CNN to estimate the geometry of the loop current models. The magnetic coupling coefficient between MSLs is estimated through calculating the coupled magnetic flux between the estimated loop current models. The estimated results agreed with the simulation results within 10%, indicating the feasibility of the magnetic field coupling by the proposed method.