Machine learning enabled the design of compact and efficient wavelength demultiplexing photonic devices

In this paper, we introduce the design approach of integrated photonic devices by employing reinforcement learning known as attractor selection (AttSel). Here, we combined 3D FDTD with AttSel algorithm, which is based on artificial neural networks, to achieve ultra-compact and highly efficient wavelength demultiplexers with low crosstalk such as. The presented devices consist of SOI materials, which are compatible with complementary MOS technology. Consequently, the reinforcement learning is successfully applied to design smaller and superior integrated photonic devices.

Keyword: inverse design; machine learning; wavelength demultiplexing