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

David Maldonado; Antonio Cantudo; Eduardo Perez; Rocio Romero-Zaliz; Emilio Perez-Bosch Quesada; Mamathamba Kalishettyhalli Mahadevaiah; Francisco Jimenez-Molinos; Christian Wenger; Juan Bautista Roldan

doi:10.3389/fnins.2023.1271956

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

Front Neurosci. 2023 Sep 19:17:1271956. doi: 10.3389/fnins.2023.1271956. eCollection 2023.

Affiliations

¹ Departamento de Electronica y Tecnologia de Computadores, Facultad de Ciencias, Universidad de Granada, Granada, Spain.
² Materials Research Department, IHP-Leibniz-Institut fuer innovative Mikroelektronik, Frankfurt an der Oder, Germany.
³ Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology Department, Brandenburg University of Technology Cottbus-Senftenberg (BTU), Cottbus, Germany.
⁴ Center for Research in Information and Communication Technologies (CITIC), Andalusian Research Institute on Data Science and Computational intelligence (DaSCI), University of Granada, Granada, Spain.

Abstract

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.

Keywords: neuromorphic computing; resistive switching devices; spike timing dependent plasticity; stochastic resonance; synaptic behavior.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The authors thank the support of the Consejeria de Conocimiento, Investigacion y Universidad, Junta de Andalucia (Spain), and the FEDER program through project B-TIC-624-UGR20. They also thank the support of the Federal Ministry of Education and Research of Germany under Grant 16ME0092.