记忆电阻器
神经形态工程学
材料科学
图层(电子)
光电子学
电压
纳米技术
电子工程
计算机科学
电气工程
工程类
人工智能
人工神经网络
作者
Xianyue Zhao,Kefeng Li,Ziang Chen,Andrea Dellith,Jan Dellith,Uwe Hübner,Christopher Bengel,Feng Liu,Stephan Menzel,Heidemarie Schmidt,Nan Du
摘要
Self-rectifying analog memristors have emerged as promising components for neuromorphic computing systems due to their inherent rectifying behavior and analog resistance states. Among these devices, BiFeO3 (BFO) memristors have shown exceptional performance, attributed to the accumulation and migration of oxygen vacancy (Vo··). However, the movement of Vo·· within the structure of the device presents challenges in optimizing their performance. To address this, the insertion of an interfacial layer has been proposed as a strategy to change the movement of Vo·· and enhance the behavior of memristor. In this study, we investigate the optimization of self-rectifying analog memristors by inserting an interfacial layer in BFO memristors. The more significant nonlinearity in high resistance state branch we observed in the current–voltage relationship leads to better rectifying behavior and a larger on/off ratio at room temperature, which indicates that the interfacial layer improves rectifying behavior. Moreover, we propose a model based on the modulation of the interfacial barrier to elucidate the impact of the interfacial layer on the BFO memristor. These findings provide insight into the design principles for optimizing self-rectifying analog memristors, with potential applications in neuromorphic computing.
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