On the modeling of thermal and free carrier nonlinearities in silicon-on-insulator microring resonators

The temporal dynamics of integrated silicon resonators has been modeled using a set of equations coupling the internal energy, the temperature and the free carrier population. Owing to its simplicity, Newton's law of cooling is the traditional choice for describing the thermal evolution of such systems. In this work, we theoretically and experimentally prove that this can be inadequate in monolithic planar devices, leading to inaccurate predictions. A new equation that we train to reproduce the correct temperature behaviour is introduced to fix the discrepancies with the experimental results. We discuss the limitations and the range of validity of our refined model, identifying those cases where Netwon's law provides, nevertheless, accurate solutions. Our modeling describes the phenomena underlying thermal and free carrier instabilities and is a valuable tool for the engineering of photonic systems which rely on resonator dynamical states, such as all optical spiking neural networks or reservoirs for neuromorphic computing.

A photonic complex perceptron for ultrafast data processing

In photonic neural network a key building block is the perceptron. Here, we describe and demonstrate a complex-valued photonic perceptron that combines time and space multiplexing in a fully passive silicon photonics integrated circuit to process data in the optical domain. A time dependent input bit sequence is broadcasted into a few delay lines and detected by a photodiode. After detection, the phases are trained by a particle swarm algorithm to solve the given task. Since only the phases of the propagating optical modes are trained, signal attenuation in the perceptron due to amplitude modulation is avoided. The perceptron performs binary pattern recognition and few bit delayed XOR operations up to 16 Gbps (limited by the used electronics) with Bit Error Rates as low as [Formula: see text].