Tunable optoelectronic response in van der Waals heterojunction transistors for artificial visual recognition

作者：Mengli Dang, Xinpei Duan, Chang Liu, Sen Zhang, Xitong Hong, Wencheng Niu, Pengfei Luo, Bei Jiang, Tong Bu, Lin Tang, Weijie Jiang, Da Wan, Xuming Zou, Lei Liao, Xingqiang Liu · 发表于：Applied Physics Letters · 年份：2024 · DOI：10.1063/5.0192497 · 被引用次数：12 · 研究领域：Advanced Memory and Neural Computing、Photoreceptor and optogenetics research、Neuroscience and Neural Engineering

Optoelectronic synaptic transistors are advantageous in in-memory light sensing for artificial neural networks. Herein, optoelectronic synaptic junction field-effect transistors (JFETs) based on a Ga2O3/MoS2 heterojunction are fabricated. The devices exhibit robust electrical performances, including a high on/off ratio of 108, a low subthreshold swing of 69 mV dec−1, and a high output current of 3.4 μA μm−1. An inverter and a NAND gate are constructed based on the dual-gated configuration, with the inverter showing a high voltage gain of 28 and the near-ideal noise margin of 90.4%. Additionally, the devices demonstrate outstanding optoelectronic performances benefiting from the strong light–matter interactions of MoS2. Typical synaptic plasticities, including short-term plasticity, long-term plasticity, and spiking-rate-dependent plasticity, are simulated by applying the light pulses. Furthermore, metaplastic excitatory postsynaptic current, metaplastic facilitation of long-term potentiation and transition from potentiation to depression are also readily demonstrated. The artificial neural network, in which neurons are interconnected through our proposed optoelectronic synaptic transistors, achieves a high accuracy of 89.8% in recognizing handwritten digits. This work provides insight into the design of an optoelectronic synapse based on JFETs.