[TuP-G] Poster Session 1

[TuP-G-4] Prediction of Supercontinuum Generation in Lithium Niobate Waveguides Using a Fully Connected Neural Network

○Haosheng Xiao¹, Feng Ye¹, H.Y. Fu², Qian Li¹ (1. School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China (China), 2. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China (China))

Keywords:

Deep learning for photonic device and applications,Photonics in neuromorphic computing and machine learning devices

A fully connected neural network (FCNN) is constructed to predict supercontinuum generation in thin-film lithium niobate waveguides. By mapping instantaneous inputs to outputs, the FCNN can efficiently and accurately predict supercontinuum generation.