Design and prediction of PIT devices through deep learning

Graphene material has excellent performance and unique variable carrier density characteristics, making it an excellent mid-infrared material. And deep learning makes it possible to quickly design mid-infrared band devices with good performance. A graphene nano-ring-symmetric sector-shaped disk array structure based on the PIT principle is proposed here for sensing. The influence of structural parameters and Fermi energy changes are studied. And its FOM (Figure Of Merit) can reach 28.7; the sensitivity is 574 cm^-1 / RIU (Refractive Index Unit). At the same time, we designed a six-layer deep learning network that can predict structural parameters and curve predictions. When predicting structural parameters, its MAPE (Mean Absolute Percentage Error) converges to 0.5. In curve prediction, MSE (Mean Square Error) converges to 1.2. It shows that predictions can be made very well. This paper proposes a symmetrical sector disk array structure and a 6-layer deep learning network. And the deep neural network designed based on the device data has good prediction accuracy under the premise of ensuring the network is simple. This will lay a good foundation for future sensor design and device acceleration optimization design.