Wavelength-multiplexed Delayed Inputs for Memory Enhancement of Microring-based Reservoir Computing

We numerically demonstrate a silicon add-drop microring-based reservoir computing scheme that combines parallel delayed inputs and wavelength division multiplexing. The scheme solves memory-demanding tasks like time-series prediction with good performance without requiring external optical feedback.

Multi-parallel-task Time-delay Reservoir Computing combining a Silicon Microring with WDM

We numerically demonstrate a microring-based time-delay reservoir computing scheme that simultaneously solves three tasks involving time-series prediction, classification, and wireless channel equalization. Each task performed on a wavelength-multiplexed channel achieves state-of-the-art performance with optimized power and frequency detuning.

Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing

Microring resonators (MRRs) are promising devices for time-delay photonic reservoir computing, but the impact of the different physical effects taking place in the MRRs on the reservoir computing performance is yet to be fully understood. We numerically analyze the impact of linear losses as well as thermo-optic and free-carrier effects relaxation times on the prediction error of the time-series task NARMA-10. We demonstrate the existence of three regions, defined by the input power and the frequency detuning between the optical source and the microring resonance, that reveal the cavity transition from linear to nonlinear regimes. One of these regions offers very low error in time-series prediction under relatively low input power and number of nodes while the other regions either lack nonlinearity or become unstable. This study provides insight into the design of the MRR and the optimization of its physical properties for improving the prediction performance of time-delay reservoir computing.

Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers

End-to-end learning has become a popular method for joint transmitter and receiver optimization in optical communication systems. Such approach may require a differentiable channel model, thus hindering the optimization of links based on directly modulated lasers (DMLs). This is due to the DML behavior in the large-signal regime, for which no analytical solution is available. In this paper, this problem is addressed by developing and comparing differentiable machine learning-based surrogate models. The models are quantitatively assessed in terms of root mean square error and training/testing time. Once the models are trained, the surrogates are then tested in a numerical equalization setup, resembling a practical end-to-end scenario. Based on the numerical investigation conducted, the convolutional attention transformer is shown to outperform the other models considered.

Impact of Free-carrier Nonlinearities on Silicon Microring-based Reservoir Computing

We quantify the impact of thermo-optic and free-carrier effects on time-delay reservoir computing using a silicon microring resonator. We identify pump power and frequency detuning ranges with NMSE less than 0.05 for the NARMA-10 task depending on the time constants of the two considered effects.

Data-Driven Modeling of Directly-Modulated Lasers

The end-to-end optimization of links based on directly-modulated lasers may require an analytically differentiable channel. We overcome this problem by developing and comparing differentiable laser models based on machine learning techniques.