Organic transistor-based neuromorphic electronics and their recent applications

Abstract

Neuromorphic technologies offer a promising pathway to address the escalating energy demands of artificial intelligence. At the system level, neuromorphic computing seeks to overcome the von Neumann bottleneck by integrating memory and processing, while neuromorphic sensing minimizes redundant data transfer by processing signals directly at the point of acquisition. Organic transistors have emerged as compelling candidates for emulating synaptic and neuronal behaviors owing to their low power consumption, flexibility, stretchability, and biocompatibility, making them particularly attractive for bio-related neuromorphic applications. This review provides an overview of organic transistor-based artificial synapses and neurons, with emphasis on the mechanisms underlying their neuromorphic behaviors. Subsequently, recent advances in applications, broadly categorized into neuromorphic computing and neuromorphic sensing, are summarized and representative bio-integrated demonstrations are highlighted. Finally, we outline key challenges at the material, device, and system levels, and discuss future opportunities for advancing organic neuromorphic electronics toward practical, biocompatible, and intelligent systems.