School of Chemical Engineering, Energy Frontier Laboratory, Sungkyunkwan University, Suwon, 16419, Korea.
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea.
Adv Sci (Weinh). 2022 May;9(14):e2200168. doi: 10.1002/advs.202200168. Epub 2022 Mar 20.
For valence change memory (VCM)-type synapses, a large number of vacancies help to achieve very linearly changed dynamic range, and also, the low activation energy of vacancies enables low-voltage operation. However, a large number of vacancies increases the current of artificial synapses by acting like dopants, which aggravates low-energy operation and device scalability. Here, mixed-dimensional formamidinium bismuth iodides featuring in-situ formed type-I band structure are reported for the VCM-type synapse. As compared to the pure 2D and 0D phases, the mixed phase increases defect density, which induces a better dynamic range and higher linearity. In addition, the mixed phase decreases conductivity for non-paths despite a large number of defects providing lots of conducting paths. Thus, the mixed phase-based memristor devices exhibit excellent potentiation/depression characteristics with asymmetricity of 3.15, 500 conductance states, a dynamic range of 15, pico ampere-scale current level, and energy consumption per spike of 61.08 aJ. A convolutional neural network (CNN) simulation with the Canadian Institute for Advanced Research-10 (CIFAR-10) dataset is also performed, confirming a maximum recognition rate of approximately 87%. This study is expected to lay the groundwork for future research on organic bismuth halide-based memristor synapses usable for a neuromorphic computing system.
对于价态变化记忆(VCM)型突触,大量空位有助于实现非常线性变化的动态范围,并且空位的低激活能也使低电压操作成为可能。然而,大量的空位通过充当掺杂剂来增加人工突触的电流,这加剧了低能量操作和器件的可扩展性。在此,报道了具有原位形成的 I 型能带结构的混合维甲脒碘化铋用于 VCM 型突触。与纯二维和零维相相比,混合相增加了缺陷密度,从而诱导出更好的动态范围和更高的线性度。此外,尽管混合相中存在大量的导电路径,但由于存在大量的缺陷,非导电路径的电导率降低。因此,基于混合相的忆阻器器件表现出优异的增强/抑制特性,不对称性为 3.15,500 个电导态,动态范围为 15,皮安级电流水平,每个尖峰的能量消耗为 61.08 aJ。还使用加拿大高级研究所-10(CIFAR-10)数据集进行了卷积神经网络(CNN)模拟,确认了约 87%的最大识别率。这项研究有望为基于有机铋卤化物的忆阻器突触在神经形态计算系统中的未来研究奠定基础。