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Prediction of IC Equivalent Magnetic Dipoles Using Deep Convolutional Neural Network

机译:基于深度卷积神经网络的IC等效磁偶极子预测

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The equivalent dipole model based on near electromagnetic field scanning can be used for electromagnetic interference sources reconstruction without considering the specific circuit structure. This paper presents a new method for predicting the equivalent magnetic dipole by deep convolutional neural network for simulation of IC electromagnetic radiation. The numerical testing results demonstrate that the equivalent magnetic dipole array predicted by CNN can produce the original radiation field effectively.
机译:基于近电磁场扫描的等效偶极模型可用于电磁干扰源重建而不考虑特定电路结构。本文介绍了一种新的方法,用于通过深卷积神经网络预测等效磁性偶极仿真仿真IC电磁辐射。数值测试结果表明,CNN预测的等效磁偶极阵列可以有效地产生原始辐射场。

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