Near-infrared-detectable artificial synapses for advanced neuromorphic vision applications - Minjun Choi; Gwoncheol Choi; Seungbeom Lee; Tae-Woo Lee; Hea-Lim Park (JPhys Materials)
The integration of near-infrared (NIR) light detection with artificial synaptic devices holds immense potential for advancing neuromorphic vision systems, enabling energy-efficient and high-speed data processing beyond conventional von Neumann architectures. NIR wavelengths provide critical information that visible light cannot offer owing to its high permeability and low scattering properties. This capability is particularly valuable for night vision, biomedical imaging, and autonomous sensing applications. However, existing artificial visual systems face challenges such as data transfer bottlenecks and high energy consumption, due to the separation of sensors and processors, as well as the need for digital conversion processes. NIR-responsive artificial synapses address these limitations by integrating NIR optical detection with synaptic computation, mimicking biological neural processing to achieve real-time data integration and adaptive learning. This review provides a comprehensive overview of recent advancements in NIR-detectable artificial synapses. We begin by discussing the fundamental biological synaptic properties essential for artificial synapse operation. Next, we explore the NIR-responsive materials employed in artificial synapses and the principles enabling their synaptic properties, with particular attention to device architectures. Additionally, we examine two practical applications including night vision systems and robotic control systems. Finally, we address the remaining challenges facing the field and propose future research directions for the development of this promising technology.