Behavioral modeling of multilevel HfO2-based memristors for neuromorphic circuit simulation

Antonio J. Pérez-Ávila, G. González-Cordero, E. Pérez, E. Quesada, Mamathamba Kalishettyhalli Mahadevaiaha, C. Wenger, J. Roldán, F. Jiménez-Molinos
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引用次数: 7

Abstract

An artificial neural network based on resistive switching memristors is implemented and simulated in LTspice. The influence of memristor variability and the reduction of the continuous range of synaptic weights into a discrete set of conductance levels is analyzed. To do so, a behavioral model is proposed for multilevel resistive switching memristors based on Al-doped HfO2 dielectrics, and it is implemented in a spice based circuit simulator. The model provides an accurate description of the conductance in the different conductive states in addition to describe the device-to-device variability.
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神经形态电路仿真中基于hfo2的多电平记忆电阻器的行为建模
在LTspice中实现并仿真了一种基于电阻开关忆阻器的人工神经网络。分析了忆阻变异性和将突触权值的连续范围减小为离散电导电平集的影响。为此,提出了一种基于al掺杂HfO2电介质的多电平电阻开关忆阻器的行为模型,并在spice电路模拟器中实现。除了描述器件间的可变性外,该模型还提供了对不同导电状态下电导的准确描述。
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