Near-infrared organic photoelectrochemical synaptic transistors by wafer-scale photolithography for neuromorphic visual system
Xü Liu, Shilei Dai, Yiyang Jin, Junyao Zhang, Ziyi Guo, Tongrui Sun, Li Li, Pu Guo, Huaiyu Gao, Haixia Liang, Shiqi Zhang, Lize Xiong, Yanmin Zhou, Jia Huang
Abstract
Optoelectronic synapses can be crucial for advancing artificial intelligence and visual systems. Optoelectronic synapses based on organic field-effect transistors have been widely studied but still face significant challenges including obvious programming nonlinearity, restricted response wavelength, high operation voltage, and limited storage memory. Organic electrochemical transistors can be another candidate but lack intensive studies. Additionally, wafer-scale photolithographic fabrication on optoelectronic synapses responding to near-infrared (NIR) light is highly desirable but rarely reported. Here, we propose the NIR organic photoelectrochemical transistor (OPECT) array capable of low voltage multi-level memories fabricated by photolithography. Based on NIR photo-induced electrochemical doping mechanism, the OPECTs enable linear weight programming with ultra-low nonlinearity (−0.015) over a wide range (47.3). We further demonstrate OPECTs arrays for image sensing, memorization, and visualization. Eventually, a convolutional computing system is constructed, executing accurate recognition of noisy handwritten digits. This work offers a promising insight into neuromorphic sensory computing applications. Optoelectronic synapses are crucial for advanced visual systems but are hindered by limited storage memory. Here, the authors propose organic photoelectrochemical transistor array capable of multi-level memories for neuromorphic visual computing.