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

ABSTRACT 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 WS 2 /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 WS 2 /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 WS 2 /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 WS 2 /ZTO in‐sensor RC platform, advancing neuromorphic processing for next‐generation edge technologies.