Lee Taehun, Kim Hae-In, Cho Yoonjin, Lee Sangwoo, Lee Won-Yong, Bae Jin-Hyuk, Kang In-Man, Kim Kwangeun, Lee Sin-Hyung, Jang Jaewon
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
The Institute of Electronic Technology, Kyungpook National University, Daegu 41566, Republic of Korea.
Nanomaterials (Basel). 2023 Aug 27;13(17):2432. doi: 10.3390/nano13172432.
Yttrium oxide (YO) resistive random-access memory (RRAM) devices were fabricated using the sol-gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the YO RRAM devices was investigated, and the results revealed that the resistance values gradually decreased with increasing set current compliance values. By regulating these values, the formation of pure Ag conductive filament could be restricted. The dominant oxygen ion diffusion and migration within YO leads to the formation of oxygen vacancies and Ag metal-mixed conductive filaments between the two electrodes. The filament composition changes from pure Ag metal to Ag metal mixed with oxygen vacancies, which is crucial for realizing multilevel cell (MLC) switching. Consequently, intermediate resistance values were obtained, which were suitable for MLC switching. The fabricated YO RRAM devices could function as a MLC with a capacity of two bits in one cell, utilizing three low-resistance states and one common high-resistance state. The potential of the YO RRAM devices for neural networks was further explored through numerical simulations. Hardware neural networks based on the YO RRAM devices demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This indicates that the proposed RRAM can be utilized as a memory component in practical neuromorphic systems.
采用溶胶 - 凝胶工艺在氧化铟锡/玻璃衬底上制备了氧化钇(YO)电阻式随机存取存储器(RRAM)器件。这些器件展现出传统的双极RRAM特性,无需高压形成工艺。研究了电流依从性对YO RRAM器件的影响,结果表明,随着设定电流依从性值的增加,电阻值逐渐降低。通过调节这些值,可以限制纯银导电细丝的形成。YO内部占主导地位的氧离子扩散和迁移导致在两个电极之间形成氧空位和银金属混合导电细丝。细丝组成从纯银金属变为与氧空位混合的银金属,这对于实现多级单元(MLC)切换至关重要。因此,获得了适合MLC切换的中间电阻值。所制备的YO RRAM器件可以在一个单元中作为具有两位容量的MLC发挥作用,利用三个低电阻状态和一个共同的高电阻状态。通过数值模拟进一步探索了YO RRAM器件在神经网络方面的潜力。基于YO RRAM器件的硬件神经网络展示了有效的数字图像分类,准确率约为88%,与理想的基于软件的分类(~92%)相当。这表明所提出的RRAM可以用作实际神经形态系统中的存储组件。