Li Menglin, Peng Jia, Jing Yuyu, Yan Yiran, Wang Cheng, Hou Wenjun, Cao Weiran, Wang Shuangpeng, Zhong Haizheng
MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, School of Materials Science & Engineering, Beijing Institute of Technology, Beijing 100081, China.
TCL Corporate Research, Shenzhen, Guangdong 518067, China.
J Phys Chem Lett. 2024 Oct 17;15(41):10334-10340. doi: 10.1021/acs.jpclett.4c02446. Epub 2024 Oct 7.
Brain-inspired electronics with synaptic functions hold significant promise for advancing artificial intelligent applications. In this study, we demonstrate the synaptic feature of quantum-dot light-emitting diodes (QLEDs), which can convert electrical pulses into synapse-like light signals (the brightness gradually increases as the electrical pulses are prolonged). These features are analogous to learning and forgetting in biological synapses. The enhancement of brightness can be attributed to the reduction of charge transfer from the quantum dots to ZnO electron transport layer and resistive switching effect. With an integrated complementary metal-oxide-semiconductor (CMOS) drive, arrayed synaptic QLEDs can simulate the visualization of brain-like learning processes, which can reduce the noise toward high image recognition rate (>95.0%) by deep neural networks. Our findings introduce a novel brain-inspired optoelectronic approach with potential applications in optical neuromorphic systems.
具有突触功能的受脑启发电子器件在推进人工智能应用方面具有巨大潜力。在本研究中,我们展示了量子点发光二极管(QLED)的突触特性,其可将电脉冲转换为类似突触的光信号(随着电脉冲延长,亮度逐渐增加)。这些特性类似于生物突触中的学习和遗忘。亮度的增强可归因于从量子点到ZnO电子传输层的电荷转移减少以及电阻开关效应。通过集成互补金属氧化物半导体(CMOS)驱动,阵列式突触QLED可模拟类脑学习过程的可视化,这可通过深度神经网络降低噪声以实现高图像识别率(>95.0%)。我们的发现引入了一种新颖的受脑启发的光电方法,在光学神经形态系统中具有潜在应用。