High-Accuracy Prediction of Metal-Insulator-Metal Metasurface with Deep
  Learning
Kaizhu Liu Hsiang-Chen Chui Changsen Sun Xue Han
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.