A deep neural network (DNN)-based method is proposed for the fast synthesis of a high-dimensional electromagnetic bandgap structure (HD-EBG) suppressing the power/ground noise in high-speed packages and printed circuit boards (PCBs). In recent EBG structures, a highly flexible design with high dimensionality is required, which is challenging to rapidly achieve an optimal solution. The proposed synthesis method is developed to solve this issue efficiently. The DNN hyperparameter optimization (HPO) associated with the HD-EBG structure is devised using the orthogonal array of the Taguchi method. By using this approach, the search space for HPO is reduced. For the efficient synthesis of the HD-EBG structure, an inverse modeling approach based on a DNN forward model and genetic algorithm optimization is proposed. The proposed DNN inverse modeling (DIM) is applied to two design examples where the HD-EBG structures with a constant fractional bandwidth and stopband bandwidth extension are synthesized. In the demonstration cases, the proposed DIM achieves the fast and accurate synthesis of the HD-EBG structures compared to the conventional method. With the proposed method, the synthesis time is maximally reduced up to 99.8% compared to conventional synthesis based on the forward model of full-wave simulation.

With rapid advances in technology, the operating frequencies of digital systems have increased to several GHz bands. This has led to an increase in simultaneous switching noise(SSN). To reduce SSN, electromagnetic bandgap(EBG) structures have been intensively studied. One of the critical steps in the design of an EBG structure is to predict the stopband that reduces SSN. Existing methods include using a 3D electromagnetic field simulation program or equations based on the Floquet theory. However, these have limitations. In this study, we verified a new method for predicting the stopband using a convolutional neural network(CNN). Specifically, a CNN architectural model was used to compare structures that perform well in predicting the stopband. It was also used to confirm that the DenseNet showed high performance.

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