High-Speed Neuromorphic Reservoir Computing Based on a Semiconductor Nanolaser With Optical Feedback Under Electrical Modulation

Abstract

A high-speed neuromorphic reservoir computing system based on a semiconductor nanolaser with optical feedback (SNL-based RC) under electrical modulation is proposed for the first time and demonstrated numerically. A Santa-Fe chaotic time series prediction task is employed to quantify the prediction performance of the SNL-based RC system. The effects of the Purcell cavity-enhanced spontaneous emission factor F and the spontaneous emission coupling factor β on the proposed RC system are analyzed extensively. It is found that, in general, increased F and β extend the range of good prediction performance of the SNL-based RC system. Moreover, the influences of bias current and feedback phase are also considered. Due to the ultra-short photon lifetime in SNL, the information processing rate of the SNL-based RC system reaches 10Gpbs. The proposed high-speed SNL-based RC system in this paper provides theoretical guidelines for the design of RC-based integrated neuromorphic photonic systems.