Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons

We demonstrate that time-delayed nonlinear effects in exciton polaritons can be used to construct neural networks where information is coded in optical pulses arriving consecutively on the sample. The highly nonlinear effects are induced by time-dependent interactions with the excitonic reservoir. These nonlinearities allow the creation of a nonlinear XOR logic gate that can perform operations on the picosecond timescale. An optoelectronic neural network based on the constructed logic gate performs the classification of spoken digits with a high accuracy rate.

Automated summary

This study demonstrates the utilization of time-delayed nonlinear effects in exciton polaritons to construct neural networks. Information is encoded in optical pulses arriving consecutively on the sample. The highly nonlinear effects are induced by time-dependent interactions with the excitonic reservoir, enabling the creation of a nonlinear XOR logic gate on a picosecond timescale. An optoelectronic neural network based on this logic gate achieves high accuracy in spoken digit classification.