Enhanced Prediction Performance of a Neuromorphic Reservoir Computing System Using a Semiconductor Nanolaser With Double Phase Conjugate Feedbacks
Enhanced Prediction Performance of a Neuromorphic Reservoir Computing System Using a Semiconductor Nanolaser With Double Phase Conjugate Feedbacks
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DOI：
10.1109/jlt.2020.3023451
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发表时间：
2021-01
影响因子：
4.7
通讯作者：
Xingxing Guo;S. Xiang;Yan Qu;Yanan Han;A. Wen;Y. Hao
中科院分区：
文献类型：
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作者：
Xingxing Guo;S. Xiang;Yan Qu;Yanan Han;A. Wen;Y. Hao
首次提出了一种基于半导体纳米激光器和双相位共轭反馈的神经形态水库计算系统，并进行了数值模拟。通过Santa Fe混沌时间序列预测任务研究了这种RC系统的预测性能。在速率方程中引入了珀塞尔腔增强自发辐射因子F和自发辐射耦合因子β，分析了F和β对RC系统预测性能的影响。为了比较的目的，SNL为基础的RC系统的预测性能与单PCF也被认为是。仿真结果表明，与基于SNL的单PCF RC系统相比，基于SNL的双PCF RC系统可以获得更好的预测性能。此外，还考虑了偏置电流、输入信号调制深度、反馈强度以及反馈延迟等因素的影响。本文提出的基于SNL的双光子晶体光纤RC系统具有发展基于RC的神经态光子集成电路的潜力。
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 β are included in the rate equations, and the influences of F and β 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.