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

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 pr...

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Published in2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS) pp. 1 - 6
Main Authors Perez-Avila, Antonio J., Gonzalez-Cordero, Gerardo, Perez, Eduardo, Quesada, Emilio Perez-Bosch, Kalishettyhalli Mahadevaiah, Mamathamba, Wenger, Christian, Roldan, Juan B., Jimenez-Molinos, Francisco
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.11.2020
Subjects
artificial neural networks
compact modeling
electrical model
Hafnium oxide
Integrated circuit modeling
Mathematical model
memristor
Memristors
multilevel
Neurons
Resistance
spice simulation
Switches
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Summary: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 HfO 2 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.
ISSN:2640-5563
DOI:10.1109/DCIS51330.2020.9268652