Photonic reservoir computing offers a promising route toward ultrafast, low-energy temporal signal processing, but its performance is intrinsically limited by the nonlinear characteristics and noise of its nodes. Conventional silicon microring reservoirs suffer from high optical loss and thermo-optic fluctuations, which raise the normalized mean square error (NMSE) and reduce the signal-to-noise ratio (SNR). While silicon nitride (SiN) mitigates optical losses, it has the weakness of lacking inherent nonlinearity and bistability. Here, we incorporate a monolayer of WSe2 onto a SiN microring resonator (MRR) to enhance the third-order susceptibility, resulting in a sharper resonance response and a deeper bistable potential. This hybrid integration improves dynamic stability against noise, suppresses unwanted resonance drift, and enhances noise robustness. The WSe2–SiN MRR reservoirs achieve a 4 dB increase in SNR and a 30% reduction in NMSE. By integrating with 2D material layers, training accuracy reaches ∼94% in 30–40 iterations, which is over five times faster than the bare SiN counterpart. We evaluated time-series prediction using the NARMA-10 and NARMA-20 benchmarks. Both simulations and experiments confirm robust hysteresis, longer memory retention, and stable operation exceeding 5000 cycles, highlighting a practical pathway toward noise-tolerant, energy-efficient photonic reservoirs for high-speed edge computing and signal prediction.
INAMDAR et al. (Sun,) studied this question.