Enhanced performances of photonic reservoir computing using a semiconductor laser with random distributed optical feedback.

We propose and experimentally demonstrate a photonic time-delay reservoir computing (TDRC) system with random distributed optical feedback under optical injection. To evaluate the performance, we calculate the memory ability and perform two benchmark tasks, i.e., chaotic time series prediction and nonlinear channel equalization task. Our numerical results show that the proposed TDRC has a superior performance compared with the case with conventional single optical feedback. This is attributed to the fact that the random distributed optical feedback offers multiple external cavity modes, which enhance the nonlinearity of the reservoir laser. Additionally, the experimental result also shows that our proposed TDRC scheme outperforms the computer with single optical feedback in the chaotic time series prediction task. To the best of our knowledge, our work offers a novel path to improve the performance of TDRC by introducing random distributed optical feedback.

High-speed photonic reservoir computer based on a delayed Fano laser under electrical modulation.

We propose and numerically demonstrate a high-speed photonic reservoir computing (RC) system using a compact Fano laser (FL) with optical feedback under electrical modulation. Benefiting from its insensitivity to external feedback, an FL has a wider dynamic steady-state region compared with a conventional Fabry-Perot laser, which significantly extends the ranges of desirable RC implementation. Interestingly, we observe two separate regions of good RC performances corresponding to two scenarios of the dynamic steady state of the FL, respectively. Moreover, the robust RC performance versus the feedback phase can be achieved in one of the steady-state regions, where the laser is not destabilized for lower external reflectivity. Owing to the ultra-short photon lifetime in the FL, the information processing rate of our proposed RC system may reach 10 Gbps. More importantly, as a specific type of microscopic laser, the FL offers potential applications to RC-based integrated neuromorphic photonic systems.

Enhancing time-delay suppression in a semiconductor laser with chaotic optical injection via parameter mismatch.

Time-delay signature (TDS) suppression of an external-cavity semiconductor laser (ECSL) is important for chaos-based applications and has been widely studied in the literature. In this paper, the chaotic output of an ECSL is injected into a semiconductor laser and TDS suppression in the regenerated time series is revisited. The focus of the current work is the influence of parameter mismatch on the TDS evolution, which is investigated experimentally and compared systematically to simulations. The experimental results demonstrate that it is much easier to achieve desired TDS suppression in the configuration composed of mismatched laser pairs. Numerical simulations confirm the validity of the experimental results. In the experiments and simulations, the influence of the injection parameters on TDS suppression is also studied and good agreement is obtained.

Photonic deep residual time-delay reservoir computing.

Time-delay reservoir computing (TDRC) represents a simplified variant of recurrent neural networks, employing a nonlinear node with a feedback mechanism to construct virtual nodes. The capabilities of TDRC can be enhanced by transitioning to a deep architecture. In this work, we propose a novel photonic deep residual TDRC (DR-TDRC) with augmented capabilities. The additional time delay added to the residual structure enables DR-TDRC superior to traditional deep structures across various benchmark tasks, especially in memory capability and almost an order of magnitude improvement in nonlinear channel equalization. Additionally, a specifically designed clipping algorithm is utilized to counteract the damage of redundant layers in deep structures, enabling the extension of the deep TDRC to dozens rather than just a few layers, with higher performance. We experimentally demonstrate the proof-of-concept with a 4-layer DR-TDRC containing 960 interrelated neurons (240 neurons per layer), based on four injection-locked distributed feedback lasers. We confirm the potential for scalable deep RC with elevated performance. Our results provide a feasible approach for expanding deep photonic computing to satisfy the boosting demand for artificial intelligence.