800 publications from this institution
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%.
With the ever-increasing demand in urban mobility and modern logistics sector, the vehicle population has been steadily growing over the past several decades. One natural consequence of the vehicle population growth is the increase in traffic congestion. Almost all (metropolitan) cities including the major ones, like Los Angeles, Beijing, New York, are suffering from heavy traffic congestion. Statistics show that, in 2015, 43 cities in China are suffering a prolonged travel time of more than 1.5 h every day during rush hours. In the meanwhile, traffic accidents are plaguing the economic development as well.
