In the current environment of the explosive growth in the amount of information, the demand for efficient information-processing methods has become increasingly urgent. We propose and numerically investigate a delay-based high-speed reservoir computing (RC) using a circular-side hexagonal resonator (CSHR) microlaser with optical feedback and injection. In this RC system, a smaller time interval can be obtained between virtual nodes, and a higher information processing rate (Rinf) can also be achieved, due to the ultra-short photon lifetime and wide bandwidth of the CSHR microlaser. The performance of the RC system was tested with three benchmark tasks (Santa-Fe chaotic time series prediction task, the 10th order Nonlinear Auto Regressive Moving Average task and Nonlinear channel equalization task). The results show that the system achieves high-accuracy prediction, even with a small number of virtual nodes (25), and is more feasible, with lower requirements for arbitrary waveform generators at the same rate. Significantly, at the high rate of 10 Gbps, low error predictions can be achieved over a large parameter space (e.g., frequency detuning in the interval 80 GHz, injected strength in the range of 0.9 variation and 2% range for feedback strength). Interestingly, it has the potential to achieve Rinf of 25 Gbps under technical advancements. Additionally, its shorter external cavity length and cubic micron scale size make it an excellent choice for large-scale photonic integration reservoir computing.

With the advent of the high-speed information age and the explosive of information, higher requirements are placed on the information processing speed. In recent years, researchers have carried out a lot of research on delay-based reservoir computing (RC) systems. Meanwhile, the improvement of information processing rate mainly revolves around the replacement of nonlinear nodes in the system. However, as the most frequently used distributed feedback semiconductor (DFB) laser, many researchers only use ordinary commercial DFB products for research, and they have not noticed the improvement of RC performance caused by changes in internal parameters of laser. With the development of photonic integration technology, the processing technology of DFB is more mature, so that the size of DFB can fabricate in the range of 100 μm-1 mm when it still generates laser, and the photon lifetime of the laser will also change. Since the shorter photon lifetime in the laser leads to a faster dynamic response, which has the potential to process higher rate of information in the RC system. According to the laser rate equation (Lang-Kobayashi), changing the internal cavity length will affect the feedback strength, injection strength and other parameters required for the laser to enter each dynamic state, which in turn affects the parameter space required for the RC system to exhibit high performance. Based on this, we studied the relationship between the internal cavity length (120 μm-900 μm) and the information processing rate of the RC system. In addition, the influence of different internal cavity lengths on the parameter space of the RC system is analyzed. The results show that when the internal cavity length is in the range of 120 μm to 171 μm, the system can achieve 20 Gbps low-error information processing; It is worth noting that when the internal cavity length is reduced from 600 μm to 128 μm, the parameter space with better prediction performance of the RC system is greatly improved. When performing the Santa-Fe chaotic time series prediction task, the normalized mean square error (<i>NMSE</i>) is less than 0.01, and the parameter range of the injection strength is increased by about 22%. The range of parameter with <i>NMSE</i> no more than 0.1 is improved by nearly 40% for 10^th order Nonlinear Auto-Regressive Moving Average (NARMA-10) task. When the number of virtual nodes is 50, the system can achieve high-precision prediction for the above two tasks. This is of great significance for the practical development of the system.

We experimentally demonstrate a photonic reservoir computing (RC) system using a passive silica microsphere cavity. The microsphere cavity exhibits a consistent nonlinear response to the non-return-to-zero signal and the multiple-level signal due to strong interference between numerous whispering gallery modes in the “over-coupling” state. Benefiting from the fact that the long photon lifetime inside the microsphere cavity provides a memory of past inputs, this photonic reservoir does not require a delayed feedback loop. We evaluate the generalization property of the RC system and obtain a correlation coefficient of 0.923. In addition, we obtain a NMSE of 0.06 for the Santa-Fe chaotic time series prediction task and a SER of 0.02 at a SNR of 12 dB for the nonlinear channel equalization task. Moreover, a microsphere cavity with a higher quality factor can provide a larger memory capacity. The application of the silica microsphere cavity as a small-volume passive device in a reservoir furnishes a new avenue for achieving a low-consumption and integrated RC system.