Silicon photonic secure communication using artificial neural network

Abstract

Optical chaotic secure communication based on silicon photonic microcavities has the advantages of high integration capability and natural compatibility with complementary metal oxide semiconductor (CMOS) technology. To overcome the difficulties of physical device fabrication and parameter matching, we propose a hybrid scheme that combines silicon photonic microcavity (SPM) chaos and artificial neural networks (ANNs) to achieve chaotic secure communication, which could significantly enhance the feasibility of silicon photonic secure communication. We trained three typical ANNs: multilayer perceptron (MLP), long short-term memory network (LSTM), and co`nvolutional neural network (CNN). Compared with the traditional Runge-Kutta-4 function, the prediction accuracy of three networks is greatly improved. All the networks provide an R2 of 99.9 %, with root-mean-square errors (RMSEs) and mean-absolute errors (MAEs) below 10−4. Furthermore, by loading non-return-to-zero (NRZ) information and using CNN to synchronise SPM chaos, 5 Mb/s, 10 Mb/s, and 15 Mb/s NRZ encryption and decryption are all realised. This can provide synchronisation coefficients of 99.8 % and clear decrypted eye diagrams. Additionally, return-to-zero (RZ), non-return-to-zero-inverted (NRZI) information are further demonstrated and a FPGA-based CNN design is given, proving the generality of CNN decryption scheme. These results could provide a useful foundation for advancing physical and secure communication with silicon photonics chips and ANNs.