Organic Thin‐Film Transistors for Neuromorphic Computing

ABSTRACT Organic Thin‐Film Transistors (OTFTs), including Organic Field‐Effect Transistors (OFETs) and Organic Electro‐Chemical Transistors (OECTs), offer clear advantages over traditional silicon‐based devices, particularly in power efficiency and biocompatibility. When combined with neuromorphic computing, which mimics the brain's event‐driven processing to improve computational efficiency, OTFTs become a powerful platform for next‐generation electronics. These devices have demonstrated strong potential as artificial synapses and neurons, showing key spike‐based performance metrics such as Excitatory Post‐Synaptic Current (EPSC), Paired‐Pulse Facilitation (PPF), and Long‐Term Potentiation (LTP). This review captures recent progress in OTFT‐based synaptic and neuronal devices, alongside an in‐depth analysis of how fabrication parameters influence neuromorphic performance. Such insights are critical for designing and optimizing organic neuromorphic systems. We examine the transition from single‐transistor synapses to multi‐transistor neuron models, including emerging organic Single Transistor Latch (STL) neurons that mimic Leaky Integrate and Fire (LIF) and related dynamics. This review also explores the development of OTFT‐based neural networks, their performance relative to Metal‐Oxide‐Semiconductor Field‐Effect Transistor (MOSFET)‐based systems, and their potential shift toward fully neuromorphic Spiking Neural Networks (SNNs). Beyond surveying device demonstrations, this review introduces a standardized characterization protocol for OTFT synapses, and maps the physical mechanisms of OTFT architectures onto appropriate learning rules and network models. By linking materials, device physics, and neuromorphic algorithms, we highlight the opportunities for co‐designing flexible, bio‐integrated OTFT‐based neuromorphic systems.