Lu Xiu Fang, Zhang Yishu, Wang Naizhou, Luo Sheng, Peng Kunling, Wang Lin, Chen Hao, Gao Weibo, Chen Xian Hui, Bao Yang, Liang Gengchiau, Loh Kian Ping
Department of Chemistry, National University of Singapore, Singapore 117543, Singapore.
Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.
Nano Lett. 2021 Oct 27;21(20):8800-8807. doi: 10.1021/acs.nanolett.1c03169. Epub 2021 Oct 13.
Memristor devices that exhibit high integration density, fast speed, and low power consumption are candidates for neuromorphic devices. Here, we demonstrate a filament-based memristor using p-type SnS as the resistive switching material, exhibiting superlative metrics such as a switching voltage ∼0.2 V, a switching speed faster than 1.5 ns, high endurance switching cycles, and an ultralarge on/off ratio of 10. The device exhibits a power consumption as low as ∼100 fJ per switch. Chip-level simulations of the memristor based on 32 × 32 high-density crossbar arrays with 50 nm feature size reveal on-chip learning accuracy of 87.76% (close to the ideal software accuracy 90%) for CIFAR-10 image classifications. The ultrafast and low energy switching of p-type SnS compared to n-type transition metal dichalcogenides is attributed to the presence of cation vacancies and van der Waals gap that lower the activation barrier for Ag ion migration.
具有高集成密度、快速速度和低功耗的忆阻器器件是神经形态器件的候选者。在此,我们展示了一种以丝状忆阻器,它使用p型SnS作为电阻开关材料,展现出诸如开关电压约为0.2 V、开关速度快于1.5 ns、高耐久性开关循环以及高达10的超大开/关比等卓越指标。该器件每个开关的功耗低至约100 fJ。基于具有50 nm特征尺寸的32×32高密度交叉阵列的忆阻器芯片级模拟显示,对于CIFAR-10图像分类,片上学习准确率为87.76%(接近理想软件准确率90%)。与n型过渡金属二硫属化物相比,p型SnS的超快和低能量开关归因于阳离子空位和范德华间隙的存在,它们降低了Ag离子迁移的激活势垒。