Abstract

Deep neural networks (DNNs) have been introduced to achieve the rapid design of photonic devices by creating a nonlinear function mapping the geometric structure to the optical response. By building the DNN with a finite-difference time-domain (FDTD) solver, we have demonstrated that both forward and inverse design approaches can be used to design efficiently a silicon photonic grating coupler—one of the fundamental silicon photonic devices with a wavelength-sensitive optical response, respectively. A systematic study on the model parameters including number of hidden layers, number of nodes in each layer, initial learning rate, size of training batches, number of evolution epochs, and dataset size/distribution has been carried out to analyze the relationship between the DNNs and the performances of inverse-designed devices. The study shows that the forward design approach based on an optimal forward-modeling network can achieve a peak coupling efficiency with a prediction accuracy as high as 91.7% for the coupler. And the inverse design approach based on an optimal inverse-prediction network can obtain target optical response spectrum as well as provide possibility to get an alternative design for the device. This work is helpful for the designers to improve the machine learning methods and expedite the design progress towards the creation of novel silicon photonic devices.

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