Highly Flexible and Asymmetric Hexagonal-Shaped Crystalline Structured Germanium Dioxide-Based Multistate Resistive Switching Memory Device for Data Storage and Neuromorphic Computing

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.

Automated summary

This paper proposes a highly flexible and asymmetric memory device for high-density data storage and neuromorphic computing. The device shows excellent stability and exhibits synaptic learning properties, making it a potential candidate for pattern recognition applications.