School of Electrical Engineering , Graphene/2D Materials Research Center, KAIST , Daejeon 34141 , Korea.
Department of Materials Science and Engineering and NUANCE Center , Northwestern University , Evanston , Illinois 60208 , United States.
Nano Lett. 2019 Feb 13;19(2):839-849. doi: 10.1021/acs.nanolett.8b04023. Epub 2019 Jan 9.
With the advent of artificial intelligence (AI), memristors have received significant interest as a synaptic building block for neuromorphic systems, where each synaptic memristor should operate in an analog fashion, exhibiting multilevel accessible conductance states. Here, we demonstrate that the transition of the operation mode in poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament. With the quantized conductance states observed in the flexible pV3D3 memristor, analog potentiation and depression characteristics of the memristive synapse are obtained through the growth of atomically thin Cu filament and lateral dissolution of the filament via dominant electric field effect, respectively. The face classification capability of our memristor is evaluated via simulation using an artificial neural network consisting of pV3D3 memristor synapses. These results will encourage the development of soft neuromorphic intelligent systems.
随着人工智能 (AI) 的出现，忆阻器作为神经形态系统的突触构建块引起了极大的关注，其中每个突触忆阻器都应该以模拟方式工作，表现出多级可访问的导通电导状态。在这里，我们证明了基于聚（1,3,5-三乙烯基-1,3,5-三甲基环三硅氧烷）（pV3D3）的柔性忆阻器的操作模式从传统的二进制到突触模拟切换的转变可以通过简单地减小形成的丝的尺寸来实现。通过在柔性 pV3D3 忆阻器中观察到的量化电导状态，通过原子层薄的 Cu 丝的生长和通过主导电场效应的丝的横向溶解，获得了忆阻突触的模拟增强和抑制特性。通过使用由 pV3D3 忆阻器突触组成的人工神经网络进行模拟，评估了我们的忆阻器的面部分类能力。这些结果将鼓励开发软神经形态智能系统。
