神经形态工程学
材料科学
光电子学
晶体管
光电流
存水弯(水管)
非易失性存储器
阈值电压
半导体
偏压
栅氧化层
纳米技术
薄膜晶体管
逻辑门
数码产品
氧化物
电子迁移率
电压
氧化锡
干扰(通信)
柔性电子器件
和大门
光电导性
锡
作者
Andrea Sessa,Sebastiano De Stefano,O. Durante,Aniello Pelella,Martino Aldrigo,Cǎtǎlin Corneliu Pârvulescu,Adrian M. Dinescu,Chia-Nung Kuo,C. S. Lue,Tsotne Dadiani,Gianluca D’Olimpio,Enver Faella,Antonio Politano,M. Passacantando,Antonio Di Bartolomeo
标识
DOI:10.1002/aelm.202500734
摘要
Abstract 2D semiconductors are attracting considerable interest for neuromorphic electronics for their strong light–matter interaction, defect‐mediated charge dynamics, and suitability for energy‐efficient devices. Among them, tin diselenide (SnSe 2 ) combines Earth abundance, environmental stability, high carrier mobility and persistent photoconductivity that make it a compelling candidate for multifunctional optoelectronic synapses. Here, we investigate multilayer SnSe 2 field‐effect transistors and demonstrate gate‐tunable optoelectronic plasticity. Systematic measurements as a function of temperature, illumination power, and gate bias reveal that the device photoresponse is dominated by trap‐assisted photogating. The interplay between fast and slow recombination channels produces a persistent photocurrent (PPC) that can be finely tuned by the gate voltage. Negative gate bias enhances charge separation and prolongs PPC, enabling long‐term potentiation, while positive gate bias accelerates recombination and suppresses persistence, yielding short‐term memory. Furthermore, short gate voltage pulses enable reversible suppression of persistent photocurrent, allowing controlled switching between short‐ and long‐term memory states. Under repetitive optical stimulation, the devices exhibit cumulative learning and memory retention with high reproducibility. These results highlight SnSe 2 as a robust platform for optoelectronic neuromorphic devices. By exploiting interfacial trap states and gate control, SnSe 2 ‐based transistors emulate essential synaptic functionalities with excellent stability, offering new opportunities for 2D‐material‐enabled scalable neuromorphic hardware.
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