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Misestimate of the performance in VCSEL-based reservoir computing systems with optical information injection by high surface reflectivity

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Abstract

In reservoir computing (RC) systems based on semiconductor lasers (SLs), the information that must be processed usually enters the reservoir through optical injection. Part of the injection information directly reflected by the front facet of the SLs is inevitably hybridized into the output of the SLs and contributes to the state of virtual nodes. For an RC system based on vertical-cavity surface-emitting lasers (VCSELs), the proportion of the reflected information coupled to the laser output is relatively huge due to the high surface reflectivity. Thus the influence of the directly reflected information will be much more obvious. Using a Santa Fe chaotic time series prediction task and waveform recognition task, we theoretically investigate the influence of high front facet reflectivity on the evaluation of the performance of a VCSEL-based RC system with optical information injection. The simulation results demonstrate that, after taking the directly reflected information into account, a lower error rate is obtained for each benchmark task. The physical mechanism to misestimate the RC performance has been studied through memory correlation and a statistical histogram of virtual node states.

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