WS ₂ Optoelectronic Memristive Reservoir Enabling Ultra‐Low‐Power, Multi‐Task, and Environmentally Stable Neuromorphic Computing

Energy-efficient visual and speech processing is essential for edge intelligence, yet conventional silicon-based chips suffer from high power consumption. Here, we report a WS₂/Zinc-Tin-Oxide (ZTO)-based optoelectronic reservoir computing (RC) system that uniquely integrates sensing, memory, and computation within a single compact device to emulate diverse biological functions. The WS₂/ZTO memristive RC achieves strong performance, with ∼94% accuracy on N-MNIST, ∼93% in motion perception, and ∼89% in speech recognition within only 30 training epochs, while consuming ultra-low energy of ∼25.5 fJ/spike. Raw inputs are converted into spike trains to preserve temporal dynamics: motion data from inter-frame differences, FSDD waveforms reshaped into spike-like signals, and N-MNIST reconstructed directly from the address-event representation format. The system maintains reliable operation under 95% relative humidity, highlighting excellent environmental stability. Distinctively, the WS₂/ZTO memristor serves simultaneously as sensor and hardware reservoir, exploiting volatile and nonlinear dynamics for direct temporal input decoding. Validation on N-MNIST further shows 95% accuracy with minimal training energy. In addition, the device demonstrates endurance over 1.5 million cycles and supports synaptic features including excitatory postsynaptic current, short-term and long-term plasticity, and photonic paired-pulse facilitation. This work establishes a humidity-resilient, ultra-low-power WS₂/ZTO in-sensor RC platform, advancing neuromorphic processing for next-generation edge technologies.