We present a comprehensive numerical model for intrinsic small-signal modulation response of both reflective, and traveling-wave semiconductor optical amplifiers. We investigate the small-signal photon and carrier density spatial distribution, modulation response, and -3 dB bandwidth for uniform and lossless traveling-wave modulation current model. The analysis shows that the current model does not significantly affect the modulation response as long as the modulation frequency is within the bandwidth. One of the most important results of our analysis is the discovery of the bandwidth maximum in case of a reflective semiconductor optical amplifier operating with low to moderate input optical powers and high current densities. The bandwidth can be further improved by choosing the optimal amplifier length.

Switching between states in a dispersive bistable injection-locked slave laser has been theoretically investigated. We show that the switching can be achieved by relatively small and short ($\approx 10–50\mathrm{ps}$) variation of the master laser injection power or frequency, which, besides the variation of the slave laser optical power, leads to significant variation of its photon phase ($\approx 5\pi /6$). By using an analytical model, we calculate the switching time dependence on the magnitude of the injection power and the frequency detuning variation.

In this Letter, we theoretically investigate the application of a bistable Fabry–Perot semiconductor laser under optical injection as an all-optical activation unit for multilayer perceptron optical neural networks. The proposed device is programmed to provide reconfigurable sigmoid-like activation functions with adjustable thresholds and saturation points and benchmarked on machine learning image recognition problems. Due to the reconfigurability of the activation unit, the accuracy can be increased by up to 2% simply by adjusting the control parameter of the activation unit to suit the specific problem. For a simple two-layer perceptron neural network, we achieve inference accuracies of up to 95% and 85%, for the MNIST and Fashion-MNIST datasets, respectively.

The paper explores the performance enhancement of a photonic reservoir computer through the reconfigurability of all-optical nonlinear node based on Fabry-Pérot laser diode. By benchmarking various tasks with differing computational demands, we demonstrate how reconfigurability offers flexible computational capabilities, enabling the reservoir computer to adapt to specific tasks while striking an optimal balance between nonlinear transformation and linear memory. We achieve the state-of-the-art results using a basic delay-line reservoir computer concept with a small number of virtual nodes (∼30 nodes).

In this Letter, we present a detailed theoretical and experimental investigation of optical bistability and tristability in dual injection-locked Fabry–Perot laser diodes. The proposed device can be reconfigured between the bistable and tristable regimes, simply by adjusting the power level of the injected control optical signal. The tristability presented in the experiment is achieved for relatively low optical input powers between 1.03 and 1.25 mW, with the output signal ratio of up to 7 dB between stable states. Such a device is a potential candidate for designing trits, a bit analogy in ternary computational logic.