Novel devices for optical convolutional neural networks

An optical neural network is the physical implementation of an artificial neural network with optical components. Realising the convolution function is particularly advantageous in this regard because optical processors can readily perform convolutions in the Fourier domain. This project focusses on the development of novel optical devices that provide the input to such networks. In particular, we will develop an array of spatial light modulators to provide the 2D input function. Each modulator will be based on an asymmetric Fabry-Perot modulator, the reflectivity of which can be controlled by carrier injection. The student will optimise the design of these modulators, fabricate them in the York Nanocentre cleanroom, then test them in the Photonics laboratories. The goal is to achieve a modulation of the reflectivity from near 0% to near 100%, which, according to initial calculations, can be realised with a very low (10-100A) current and at high speed (MHz-GHz). Following the optimisation on the basis of individual devices, the project will then realise large scale arrays (e.g. 32x32 or 64x64) and test them in a systems context. The project is a collaboration with the company Optalysys Ltd. who will provide the systems expertise.