Optical information processing: Advances in nanophotonic reservoir computing

We present a complex network of interconnected optical structures for information processing. This network is an implementation of reservoir computing, a novel method in the field of machine learning. Reservoir computing can be used for example in classification problems such as speech and image recognition, or for the generation of arbitrary patterns, tasks which are usually very hard to generalize. A nanophotonic reservoir can be constructed to perform optical signal processing. Previously, simulations demonstrated that a reservoir consisting of Semiconductor Optical Amplifiers (SOA) can outperform traditional software-based reservoirs for a speech task. Here we propose a network of coupled photonic crystal cavities. Because of the resonating behaviour, a lot of power is stored in the cavity, which gives rise to interesting nonlinear effects. Simulations are done using a novel software tool developed at Ghent University, called Caphe. We train this network of coupled resonators to generate a periodic pattern using a technique called FORCE. It is shown that photonic reservoirs can outperform classical software-based reservoirs on a pattern generation task.