Highly Flexible and Asymmetric Hexagonal‐Shaped Crystalline Structured Germanium Dioxide‐Based Multistate Resistive Switching Memory Device for Data Storage and Neuromorphic Computing. (25th June 2022)

• Record Type: Journal Article
• Authors: Chougale, Mahesh Y.; Khan, Muhammad Umair; Kim, Jungmin; Furqan, Chaudhry Muhammad; Saqib, Qazi Muhammad; Shaukat, Rayyan Ali; Patil, Swapnil R.; Mohammad, Baker; Kwok, Hoi‐Sing; Bae, Jinho
• Is Part Of: Advanced Electronic Materials. Volume 8:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-25
• Subjects: convolutional neural network -- flexible electronics -- hexagonal‐shaped crystalline GeO 2 -- multistate synaptic devices; Materials -- Electric properties -- Periodicals; Materials science -- Periodicals; Magnetic materials -- Periodicals; Electronic apparatus and appliances -- Periodicals; 537
• DOI: 10.1002/aelm.202200332
• Languages: English
• ISSNs: 2199-160X

Abstract

Abstract: With the increase of big data and artificial intelligence (AI) applications, fast and energy‐efficient computing is critical in future electronics. Fortunately, nonvolatile resistive memory devices can be potential candidates for these issues due to their in‐computing and neuromorphic computational abilities. Hence, the paper proposes a highly flexible and asymmetric hexagonal‐shaped crystalline structured germanium dioxide‐based Ag/GeO2 /ITO device for high data storage and neuromorphic computing. The proposed device shows the highly asymmetric memristor behavior at low operating voltage to block backward current. The operational behaviors are observed by modulating the applied amplitude, current compliance, and varying the frequency, which shows excellent stability and repeatability in electrical characterizations. Furthermore, the neuromorphic device exhibits synaptic learning properties such as potentiation‐depression, pulse amplification, and spike time‐dependent plasticity rules (STDP). Here, the weights update of the memristive synaptic device is analyzed using a multilayer perceptron convolutional neural network (CNN) by optimizing the learning rate, training epochs, and algorithm to achieve higher accuracy for pattern recognition using CIFAR‐10 data. Undoubtedly, the demonstrated results suggest that the proposed device is a promising candidate to develop high‐density storage and neuromorphic computing technology for wearable and AI electronics. Abstract : Artificial electronic synapses are a promising technology to develop neuromorphic computing. Herein, the hexagonal‐shaped crystalline germanium dioxide‐based memory device for the electronic synapses is proposed. The proposed device exhibits asymmetric memristive behavior with synaptic learning properties which pave the path toward flexible and soft robotics.