Aiming at the limitations of modelling and calculation methods and high consumption of computing resources of traditional power distribution network(PDN),a PDN impedance prediction method(URPNet)based on deep learning is proposed.Based on the fusion of PCB irregular shape,multi-layer information and position information of various capacitive ports,U-shaped codec structure and residual units are used to process features,and multi-layer perceptron(MLP)and fully connected(FC)layers are used to decode and reconstruct features,thereby improving the feature processing capability of the network.Experimental results show that the determination coefficient of URPNet model reaches 0.999,and the root mean square error is 0.431.Compared with existing deep learning methods,URPNet has strong universality and more accurate prediction results.In addition,the calculation speed is fast,and the prediction can be completed in less than 1 s,which can effectively meet the challenges in PDN design.
关键词
电源分配网络/目标阻抗/电源完整性/神经网络
Key words
power distribution network/target impedance/power integrity/neural network
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