Α-Fe2O3-based artificial synaptic RRAM device for pattern recognition using artificial neural networks. (25th June 2023)

• Record Type: Journal Article
• Authors: Jetty, Prabana; Mohanan, Kannan Udaya; Jammalamadaka, S Narayana
• Is Part Of: Nanotechnology. Volume 34:Number 26(2023)
• Publication Date: 2023-06-25
• Subjects: memristor device -- RRAM -- potentiation -- depression -- artificial neural networks -- spike timedependent plasticity; Nanotechnology -- Periodicals; Nanotechnology -- Periodicals; Nanotechnology; Publications périodiques; Nanotechnologies; Periodicals; 620.5
• DOI: 10.1088/1361-6528/acc811
• Languages: English
• ISSNs: 0957-4484

Abstract

Abstract: We report on the α -Fe2 O3 -based artificial synaptic resistive random access memory device, which is a promising candidate for artificial neural networks (ANN) to recognize the images. The device consists of a structure Ag/ α -Fe2 O3 /FTO and exhibits non-volatility with analog resistive switching characteristics. We successfully demonstrated synaptic learning rules such as long-term potentiation, long-term depression, and spike time-dependent plasticity. In addition, we also presented off-chip training to obtain good accuracy by backpropagation algorithm considering the synaptic weights obtained from α -Fe2 O3 based artificial synaptic device. The proposed α -Fe2 O3 -based device was tested with the FMNIST and MNIST datasets and obtained a high pattern recognition accuracy of 88.06% and 97.6% test accuracy respectively. Such a high pattern recognition accuracy is attributed to the combination of the synaptic device performance as well as the novel weight mapping strategy used in the present work. Therefore, the ideal device characteristics and high ANN performance showed that the fabricated device can be useful for practical ANN implementation.