Li Ming, Li Mingjun, An Haoqun, An Jun Seop, Gu Pengyu, Kim Dae Hun, Park Kwan Kyu, Kim Tae Whan
Department of Electronic Engineering, Hanyang University, Seoul 04763, Republic of Korea.
Research Institute of Industrial Science, Hanyang University, Seoul 04763, Republic of Korea.
ACS Appl Mater Interfaces. 2024 Jan 24;16(3):3621-3630. doi: 10.1021/acsami.3c12615. Epub 2024 Jan 10.
The metallic conductive filament (CF) model, which serves as an important conduction mechanism for realizing synaptic functions in electronic devices, has gained recognition and is the subject of extensive research. However, the formation of CFs within the active layer is plagued by issues such as uncontrolled and random growth, which severely impacts the stability of the devices. Therefore, controlling the growth of CFs and improving the performance of the devices have become the focus of that research. Herein, a synaptic device based on polyvinylpyrrolidone (PVP)/graphene oxide quantum dot (GO QD) nanocomposites is proposed. Doping GO QDs in the PVP provides a large number of active centers for the reduction of silver ions, which allows, to a certain extent, the growth of CFs to be controlled. Because of this, the proposed device can simulate a variety of synaptic functions, including the transition from long-term potentiation to long-term depression, paired-pulse facilitation, post-tetanic potentiation, transition from short-term memory to long-term memory, and the behavior of the "learning experience". Furthermore, after being bent repeatedly, the devices were still able to simulate multiple synaptic functions accurately. Finally, the devices achieved a high recognition accuracy rate of 89.39% in the learning and inference tests, producing clear digit classification results.
金属导电细丝(CF)模型作为实现电子设备中突触功能的重要传导机制,已得到认可并成为广泛研究的对象。然而,活性层内CF的形成受到诸如生长不受控制和随机等问题的困扰,这严重影响了器件的稳定性。因此,控制CF的生长并提高器件性能已成为该研究的重点。在此,提出了一种基于聚乙烯吡咯烷酮(PVP)/氧化石墨烯量子点(GO QD)纳米复合材料的突触器件。在PVP中掺杂GO QD为银离子的还原提供了大量活性中心,这在一定程度上使得CF的生长能够得到控制。因此,所提出的器件能够模拟多种突触功能,包括从长时程增强到长时程抑制的转变、双脉冲易化、强直后增强、从短期记忆到长期记忆的转变以及“学习经验”行为。此外,在反复弯曲后,这些器件仍能够准确模拟多种突触功能。最后,这些器件在学习和推理测试中实现了89.39%的高识别准确率,产生了清晰的数字分类结果。