On-chip diffractive optical neural network based on binary metasurfaces

This paper proposes and investigates an on-chip diffractive optical neural network based on binary metasurfaces. The genetic algorithm and finite-difference time-domain method are used to optimize the binary metasurfaces to achieve relatively compact area size and excellent performance. To prove the effectiveness of our proposal, a single-layer diffractive optical neural network based on binary metasurfaces is designed to execute a classification task on the Iris dataset. The area size of the designed single-layer diffractive optical neural network is only 16.5 µm × 23 µm. In the simulation, a validation accuracy of 90.0% is attained. The designed single-layer diffractive optical neural network was fabricated on a 220-nm silicon-on-insulator platform as a proof of concept. Measurement results show that the validation accuracy of the fabricated single-layer diffractive optical neural network is 78.3%.