Zhu Yuan, Liang Jia-Sheng, Shi Xun, Zhang Zhen
Division of Solid-State Electronics, Department of Electrical Engineering, Uppsala University, Uppsala 75121, Sweden.
State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 200050, China.
ACS Appl Mater Interfaces. 2022 Sep 28;14(38):43482-43489. doi: 10.1021/acsami.2c11183. Epub 2022 Sep 14.
Flexible memristor-based neural network hardware is capable of implementing parallel computation within the memory units, thus holding great promise for fast and energy-efficient neuromorphic computing in flexible electronics. However, the current flexible memristor (FM) is mostly operated with a filamentary mechanism, which demands large energy consumption in both setting and computing. Herein, we report an AgS-based FM working with distinct interface resistance-switching (RS) mechanism. In direct contrast to conventional filamentary memristors, RS in this AgS device is facilitated by the space charge-induced Schottky barrier modification at the Ag/AgS interface, which can be achieved with the setting voltage below the threshold voltage required for filament formation. The memristor based on interface RS exhibits 10 endurance cycles and 10 s retention under bending condition, and multiple level conductive states with exceptional tunability and stability. Since interface RS does not require the formation of a continuous Ag filament via Ag ion reduction, it can achieve an ultralow switching energy of ∼0.2 fJ. Furthermore, a hardware-based image processing with a software-comparable computing accuracy is demonstrated using the flexible AgS memristor array. And the image processing with interface RS indeed consumes 2 orders of magnitude lower power than that with filamentary RS on the same hardware. This study demonstrates a new resistance-switching mechanism for energy-efficient flexible neural network hardware.
基于柔性忆阻器的神经网络硬件能够在存储单元内实现并行计算，因此在柔性电子学中实现快速且节能的神经形态计算具有巨大潜力。然而，当前的柔性忆阻器（FM）大多通过丝状机制运行，这在设置和计算过程中都需要大量能耗。在此，我们报道了一种基于硫化银（AgS）的FM，其通过独特的界面电阻开关（RS）机制工作。与传统的丝状忆阻器形成直接对比的是，这种AgS器件中的RS是由Ag/AgS界面处空间电荷诱导的肖特基势垒改性所促成的，这可以在低于形成细丝所需阈值电压的设置电压下实现。基于界面RS的忆阻器在弯曲条件下表现出10次耐久性循环和10秒的数据保持能力，以及具有出色可调性和稳定性的多电平导电状态。由于界面RS不需要通过Ag离子还原形成连续的Ag细丝，它能够实现约0.2飞焦的超低开关能量。此外，使用柔性AgS忆阻器阵列展示了具有与软件相当计算精度的基于硬件的图像处理。并且在相同硬件上，基于界面RS的图像处理确实比基于丝状RS的图像处理功耗低两个数量级。这项研究展示了一种用于节能柔性神经网络硬件的新型电阻开关机制。
