Progress in Optoelectronic Synapses for Reservoir Computing: Materials, Device Integration, and Neuromorphic System Applications

ABSTRACT Reservoir computing (RC) represents a promising neuromorphic approach for efficient temporal signal processing and real‐time pattern recognition. Among hardware platforms, optoelectronic synaptic devices that integrate optical inputs with electronic plasticity offer superior bandwidth, greater parallelism, and enhanced energy efficiency. Recent advances in quantum dots, 2D materials, perovskites, and hybrid optoelectronic systems have enabled the development of artificial synapses with dynamic properties approaching those of biological systems, driving the transition from proof‐of‐concept devices to large‐scale, multifunctional array integration. This Review provides a comprehensive overview of progress in materials design, device architecture, and system integration for optoelectronic synapse‐based reservoir computing. Key developments discussed include multimodal sensory fusion, adaptive nonlinear response modulation, and scalable array fabrication, while current challenges related to scalability, reliability, and standardization are critically analyzed. The outlook for next‐generation RC hardware is explored, with emphasis on applications spanning brain–machine interfaces, wearable biomedical devices, and autonomous intelligent systems. By synthesizing advances in materials science, device physics, and computational neuroscience, this Review aims to guide interdisciplinary progress toward robust, energy‐efficient neuromorphic platforms, paving the way for transformative intelligent information processing.