High-Accuracy Prediction of Metal-Insulator-Metal Metasurface with Deep Learning
This work addresses the problem of time-consuming electromagnetic simulations for researchers and engineers in photonics and metasurface design, offering a faster alternative with high accuracy, though it is incremental as it builds on existing deep learning methods.
The paper tackled the challenge of low accuracy in deep learning predictions for electromagnetic software by using a ResNets-10 model to predict S11 parameters of metal-insulator-metal metasurfaces, achieving prediction losses as low as -48.45 for aluminum, -46.47 for gold, and -35.54 for silver.
Deep learning prediction of electromagnetic software calculation results has been a widely discussed issue in recent years. But the prediction accuracy was still one of the challenges to be solved. In this work, we proposed that the ResNets-10 model was used for predicting plasmonic metasurface S11 parameters. The two-stage training was performed by the k-fold cross-validation and small learning rate. After the training was completed, the prediction loss for aluminum, gold, and silver metal-insulator-metal metasurfaces was -48.45, -46.47, and -35.54, respectively. Due to the ultralow error value, the proposed network can replace the traditional electromagnetic computing method for calculation within a certain structural range. Besides, this network can finish the training process less than 1,100 epochs. This means that the network training process can effectively lower the design process time. The ResNets-10 model we proposed can also be used to design meta-diffractive devices and biosensors, thereby reducing the time required for the calculation process. The ultralow error of the network indicates that this work contributes to the development of future artificial intelligence electromagnetic computing software.