神经形态工程学
材料科学
光电子学
记忆电阻器
电阻式触摸屏
偏压
堆栈(抽象数据类型)
X射线光电子能谱
热传导
锡
电导
电压
电子工程
计算机科学
电气工程
复合材料
物理
凝聚态物理
核磁共振
机器学习
人工神经网络
工程类
冶金
计算机视觉
程序设计语言
作者
Sobia Ali Khan,Geun Ho Lee,Chandreswar Mahata,Muhammad Ismail,Hyungjin Kim,Sungjun Kim
出处
期刊:Nanomaterials
[Multidisciplinary Digital Publishing Institute]
日期:2021-01-27
卷期号:11 (2): 315-315
被引量:41
摘要
In this work, a ZnO-based resistive switching memory device is characterized by using simplified electrical conduction models. The conventional bipolar resistive switching and complementary resistive switching modes are accomplished by tuning the bias voltage condition. The material and chemical information of the device stack including the interfacial layer of TiON is well confirmed by transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) analysis. The device exhibits uniform gradual bipolar resistive switching (BRS) with good endurance and self-compliance characteristics. Moreover, complementary resistive switching (CRS) is achieved by applying the compliance current at negative bias and increasing the voltage at positive bias. The synaptic behaviors such as long-term potentiation and long-term depression are emulated by applying consecutive pulse input to the device. The CRS mode has a higher array size in the cross-point array structure than the BRS mode due to more nonlinear I–V characteristics in the CRS mode. However, we reveal that the BRS mode shows a better pattern recognition rate than the CRS mode due to more uniform conductance update.
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