An Estimation Method of Magnetic Coupling Coefficient Between Two Microstrip Lines Using Machine Learning of Near-Field Information
An Estimation Method of Magnetic Coupling Coefficient Between Two Microstrip Lines Using Machine Learning of Near-Field Information
复制标题
基于近场信息机器学习的两微带线磁耦合系数估计方法
DOI：
10.1109/tmag.2023.3302907
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发表时间：
2023-11
影响因子：
2.1
通讯作者：
Yusuke Sato;S. Muroga;Hidefumi Kamozawa;Motoshi Tanaka
中科院分区：
文献类型：
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作者：
Yusuke Sato;S. Muroga;Hidefumi Kamozawa;Motoshi Tanaka
研究了一种利用近场信息估计印制电路板级迹线间磁耦合系数的新方法。采用两条不同间距的平行微带线作为测试平台。将信号线中流过的电流及其回流建模为一个简单的单匝等效回路电流模型，该模型具有均匀的电流分布。首先，一个1-D卷积神经网络（CNN）的回归预测训练的理论值的磁近场分布从回路电流模型。接下来，将在1GHz下平行的两个MSL上方测量的磁近场分布输入到训练的CNN中，以估计回路电流模型的几何形状。通过计算估计的回路电流模型之间的耦合磁通来估计两个MSL之间的磁耦合系数。由实测磁场近场分布估算的环电流模型间的磁耦合系数与全波有限元法（FEM）仿真计算的耦合系数在10%以内吻合，表明该方法估算磁场耦合的可行性。
A novel 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 were modeled as a simple one-turn equivalent loop current model with uniform current distribution. First, a 1-D 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 measured 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 two MSLs is estimated through calculating the coupled magnetic flux between the estimated loop current models. The magnetic coupling coefficients between the loop current models estimated by measured magnetic near-field distribution agreed with the coupling coefficients calculated by the full-wave finite element method (FEM) simulation within 10%, which indicates the feasibility of estimating the magnetic field coupling by the proposed method.