Enhanced Performance of Reservoir Computing Using Multiple Self-Injection and Mutual Injection VCSELs

We propose and numerically demonstrate two reservoir computing (RC) schemes using multiple vertical-cavity surface-emitting lasers (VCSELs) with different external injections, i.e., self-injection and mutual injection. The virtual nodes are obtained from the orthogonal polarization modes of multiple VCSELs. We evaluate the performance of our proposed RC systems by using the chaotic time-series prediction and memory capacity (MC). Similar to edge-emitting lasers (EELs), the multiple VCSEL-based RC scheme exhibits better computing performance compared with a single VCSEL using several delay times. More interestingly, numerical results show that our proposed scheme using dual-mode is superior to that using single-mode under the scenario of the same number of virtual nodes, and the scheme using mutual injection VCSELs outperforms that using self-injection VCSELs, in which the coupling mechanism between both polarization modes may play an important role. Further, the effects of different polarized feedback/coupling configurations on both reservoir schemes are discussed in terms of the prediction performance and MC. Additionally, we demonstrate for the first time that the polarization mode competition significantly affects the parallel VCSEL-based RC performance through analyzing its dependence on the feedback phase.