Yang Qian, Yang Huihuang, Lv Dongxu, Yu Rengjian, Li Enlong, He Lihua, Chen Qizhen, Chen Huipeng, Guo Tailiang
Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China.
Zhicheng College, Fuzhou University, Fuzhou 350002, China.
ACS Appl Mater Interfaces. 2021 Feb 24;13(7):8672-8681. doi: 10.1021/acsami.0c22271. Epub 2021 Feb 10.
In recent years, much attention has been focused on two-dimensional (2D) material-based synaptic transistor devices because of their inherent advantages of low dimension, simultaneous read-write operation and high efficiency. However, process compatibility and repeatability of these materials are still a big challenge, as well as other issues such as complex transfer process and material selectivity. In this work, synaptic transistors with an ultrathin organic semiconductor layer (down to 7 nm) were obtained by the simple dip-coating process, which exhibited a high current switch ratio up to 10, well off state as low as nearly 10 A, and low operation voltage of -3 V. Moreover, various synaptic behaviors were successfully simulated including excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, and long-term depression. More importantly, under ultrathin conditions, excellent memory preservation, and linearity of weight update were obtained because of the enhanced effect of defects and improved controllability of the gate voltage on the ultrathin active layer, which led to a pattern recognition rate up to 85%. This is the first work to demonstrate that the pattern recognition rate, a crucial parameter for neuromorphic computing can be significantly improved by reducing the thickness of the channel layer. Hence, these results not only reveal a simple and effective way to improve plasticity and memory retention of the artificial synapse via thickness modulation but also expand the material selection for the 2D artificial synaptic devices.
近年来，基于二维（2D）材料的突触晶体管器件因其低维度、同时读写操作和高效率等固有优势而备受关注。然而，这些材料的工艺兼容性和可重复性仍然是一个巨大的挑战，同时还存在诸如复杂的转移过程和材料选择性等其他问题。在这项工作中，通过简单的浸涂工艺获得了具有超薄有机半导体层（低至7纳米）的突触晶体管，其显示出高达10的高电流开关比、低至近10⁻⁹ A的良好关态以及-3 V的低工作电压。此外，成功模拟了各种突触行为，包括兴奋性突触后电流、双脉冲易化、长时程增强和长时程抑制。更重要的是，在超薄条件下，由于缺陷的增强效应以及栅极电压对超薄有源层的可控性提高，获得了优异的记忆保持性和权重更新的线性度，这导致模式识别率高达85%。这是第一项证明通过减小沟道层厚度可以显著提高神经形态计算的关键参数——模式识别率的工作。因此，这些结果不仅揭示了一种通过厚度调制来改善人工突触可塑性和记忆保持性的简单有效方法，还扩展了二维人工突触器件的材料选择。
