Enhanced Prediction Performance of a Neuromorphic Reservoir Computing System Using a Semiconductor Nanolaser With Double Phase Conjugate Feedbacks

A neuromorphic reservoir computing (RC) system using a semiconductor nanolaser (SNL) with double phase conjugate feedbacks (PCF) is proposed for the first time and demonstrated numerically. The prediction performance of such RC system is investigated via Santa Fe chaotic time series prediction task. The Purcell cavity-enhanced spontaneous emission factor F and the spontaneous emission coupling factor beta are included in the rate equations, and the influences of F and beta on the prediction performance of such RC system are analyzed extensively. For the purpose of comparison, the prediction performance of SNL-based RC system with single PCF is also considered. The simulation results indicate that, compared with the SNL-based RC system with single PCF, enhanced prediction performance can be obtained for the SNL-based RC system with double PCF. Moreover, the influences of bias current, the modulation depth of input signal, feedback strength, as well as feedback delay, are also taken into account. The proposed SNL-based RC system subject to double PCF in this paper has the potential to develop the RC-based neuromorphic photonic integrated circuit.

Automated summary

A novel neuromorphic reservoir computing system using a semiconductor nanolaser with double phase conjugate feedbacks is proposed and demonstrated numerically, showing enhanced prediction performance compared to systems with single feedback. Factors such as bias current, modulation depth, feedback strength, and delay are considered, with the system having the potential to develop into a neuromorphic photonic integrated circuit.