Tu Li, Yuan Sijian, Xu Jiawei, Yang Kunlong, Wang Pengfei, Cui Xiaolei, Zhang Xin, Wang Jiao, Zhan Yi-Qiang, Zheng Li-Rong
State Key Laboratory of ASIC and System, SIST, Fudan University 200433 Shanghai China
School of Technology and Health, Royal Institute of Technology SE-10044 Stockholm Sweden.
RSC Adv. 2018 Jul 25;8(47):26549-26553. doi: 10.1039/c8ra04403a. eCollection 2018 Jul 24.
In this work, a wide-range operating synaptic device based on organic ferroelectricity has been demonstrated. The device possesses a simple two-terminal structure by using a ferroelectric phase-separated polymer blend as the active layer and gold/indium tin oxide (ITO) as the top/bottom electrodes, and exhibits a distinctive history-dependent resistive switching behavior at room temperature. And the device with low energy consumption (∼50 fJ μm per synaptic event) can provide a reliable synaptic function of potentiation, depression and the complex memory behavior simulation of differential responses to diverse stimulations. In addition, using simulations, the accuracy of 32 × 32 pixel image recognition is improved from 76.21% to 85.06% in the classical model Cifar-10 with 1024 levels of the device, which is an important step towards the higher performance goal in image recognition based on memristive neuromorphic networks.
在这项工作中，已经展示了一种基于有机铁电性的宽范围操作突触器件。该器件具有简单的双端结构，使用铁电相分离聚合物共混物作为有源层，金/氧化铟锡（ITO）作为顶部/底部电极，并在室温下表现出独特的历史依赖性电阻开关行为。并且该低能耗器件（每个突触事件约50 fJ/μm）可以提供可靠的突触功能，包括增强、抑制以及对不同刺激的差异响应的复杂记忆行为模拟。此外，通过模拟，在具有1024个器件级别的经典Cifar-10模型中，32×32像素图像识别的准确率从76.21%提高到了85.06%，这是朝着基于忆阻神经形态网络的更高性能图像识别目标迈出的重要一步。
