神经形态工程学
纳米激光器
光子学
调制(音乐)
计算机科学
联轴节(管道)
光电子学
电子工程
物理
材料科学
工程类
激光阈值
人工神经网络
人工智能
声学
冶金
波长
作者
Xing Guo,Shuiying Xiang,Ya Hui Zhang,Lin Lin,Aijun Wen,Yue Hao
标识
DOI:10.1109/jstqe.2020.2987077
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI