Created by sebastien.popoff on 27/10/2020
Inverse design of planar optical components using deep learning
Photonic integrable circuits are basically waveguide structures that allow performing useful operations, such as mode or wavelength multiplexing/demultiplexing in the case of telecommunication applications. For many operations, we can find quite easy solutions, where the shape of the structure imposes certain boundary conditions that force light to behave the way we want. However, for an arbitrary operation, it is not always possible to find a trivial solution. Non-trivial solutions, where the link between the geometry of the structure and its function is not direct, should then be considered. In the present paper, the authors use deep learning to find geometrical configurations for planar photonic circuits that look like disordered waveguides but actually perform a previously chosen linear operation. These configurations lead experimentally to robust, high throughput, and accurate behaviors.