TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance

Frontiers in Neuroscience

Neuroscience

Rocio Romero-Zaliz¹  Antonio Cantudo²  David Maldonado²  Francisco Jimenez-Molinos²  Juan Bautista Roldan²  Mamathamba Kalishettyhalli Mahadevaiah³  Emilio Perez-Bosch Quesada³  Eduardo Perez⁴  Christian Wenger⁴ 

[1] Center for Research in Information and Communication Technologies (CITIC), Andalusian Research Institute on Data Science and Computational intelligence (DaSCI), University of Granada, Granada, Spain;Departamento de Electronica y Tecnologia de Computadores, Facultad de Ciencias, Universidad de Granada, Granada, Spain;Materials Research Department, IHP-Leibniz-Institut fuer innovative Mikroelektronik, Frankfurt an der Oder, Germany;Materials Research Department, IHP-Leibniz-Institut fuer innovative Mikroelektronik, Frankfurt an der Oder, Germany;Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology Department, Brandenburg University of Technology Cottbus-Senftenberg (BTU), Cottbus, Germany;

关键词: resistive switching devices;    neuromorphic computing;    synaptic behavior;    spike timing dependent plasticity;    stochastic resonance;   

DOI  :  10.3389/fnins.2023.1271956

 received in 2023-08-03, accepted in 2023-09-01,  发布年份 2023

来源: Frontiers

【 摘 要 】

We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics.

【 授权许可】

Unknown   
Copyright © 2023 Maldonado, Cantudo, Perez, Romero-Zaliz, Perez-Bosch Quesada, Mahadevaiah, Jimenez-Molinos, Wenger and Roldan.