An iterative optimization for decoupling capacitor placement on a power delivery network (PDN) is presented based on Genetic Algorithm (GA) and Artificial Neural Network (ANN). The ANN is first trained by an appropriate set of results obtained by a commercial simulator. Once the ANN is ready, it is used within an iterative GA process to place a minimum number of decoupling capacitors for minimizing the differences between the input impedance at one or more location, and the required target impedance. The combined GA–ANN process is shown to effectively provide results consistent with those obtained by a longer optimization based on commercial simulators. With the new approach the accuracy of the results remains at the same level, but the computational time is reduced by at least 30 times. Two test cases have been considered for validating the proposed approach, with the second one also being compared by experimental measurements.

中文翻译：

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迭代遗传算法与机器学习相结合的有效PCB解耦优化

提出了一种基于遗传算法（GA）和人工神经网络（ANN）的去耦电容器在供电网络（PDN）上的迭代优化方法。首先通过商业模拟器获得的一组适当的结果来训练ANN。ANN准备就绪后，将在迭代GA过程中使用它放置最小数量的去耦电容器，以最小化一个或多个位置的输入阻抗与所需目标阻抗之间的差异。GA-ANN组合过程显示出可有效提供与基于商用模拟器进行的较长优化所获得的结果一致的结果。使用新方法，结果的准确性保持在同一水平，但是计算时间至少减少了30倍。