A single-walled carbon nanotube-based phototransistor for neuromorphic vision applications

Abstract

With the continuous expansion of sensor networks, a vast amount of unstructured data is being generated, leading to frequent data transfers between sensors and computing units. This imposes significant challenges in terms of energy consumption, latency, storage, bandwidth, and data security. To address these issues, artificial synaptic devices have emerged as a research focus in neuromorphic hardware systems due to their suitability for novel parallel computing architectures, which offer higher efficiency and energy performance compared to the traditional von Neumann architecture when handling complex, large-scale information processing tasks. In this study, we present an inkjet-printed optoelectronic synaptic thin-film transistor (OSTFT) based on semiconducting single-walled carbon nanotubes (sc-SWCNTs), employing aluminum oxide (Al₂O₃) as the gate dielectric and a photoresponsive organic semiconductor material, C₇₄H₈₀N₆S₄ (OP064), as the light-sensitive layer. The device is capable of emulating key biological synaptic functions under both optical and electrical stimulation. A range of synaptic plasticity behaviors, including excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), paired-pulse facilitation (PPF), long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP), have been systematically demonstrated. Furthermore, leveraging these synaptic functionalities, a spiking neural network (SNN) was constructed and validated through simulation for image classification on the MNIST dataset, exhibiting promising performance. This work highlights the potential of inkjet-printed sc-SWCNT-based OSTFTs incorporating OP064 as a photoresponsive medium in neuromorphic computing applications and provides a viable path toward high-efficiency, low-latency intelligent information processing systems.