Reversible training of waveguide-based AND/OR gates for optically driven artificial neural networks using photochromic molecules

Artificial neural networks (ANNs) are inspired by the biological nervous system. The high performance of such ANNs is achieved through the dynamic change of the synaptic weights by applying self-optimizing learning algorithms. Despite the simple operations for each single element in an ANN, a network with a huge number of simulated elements consumes lots of computing capacity using von Neumann computer architectures. To overcome this issue,...