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

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.

Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy‐Efficient Neuromorphic Hardware

“…The 400 neurons of the input layer correspond to the input black which is comparable to that of ideal devices and surpasses other existing memristors. [50][51][52][53][54] This can be attributed to the highly linear and symmetric multiple conductance states of our NiPS 3 memristor when subjected to the nonidentical pulse amplitude train, showcasing its potential for implementing ANNs in neuromorphic computing applications. Considering the recent development of rapid methods for the large-scale synthesis of NiPS 3 material, [55,56] as well as the outstanding memristive characteristics, we believe that high-density integration of NiPS 3 memristor arrays can offer unprecedented opportunities for various practical applications, which will be investigated in future work.…”

Emulation of Pavlovian conditioning and pattern recognition through fully connected neural networks using Holmium oxide (Ho2O3) based synaptic RRAM device

“…To evaluate the feasibility of our fabricated device for neuromorphic applications, the conductance value of LTP (P) at +1.2 V with a pulse width 50 μs and LTD (D) −1.7 V with a pulse width 50 μs was used and the results are shown in figure 6(c). The non-linearity factor (α) is defined as the nonlinearity between actual and ideal linear weight functions [35,[37][38][39] G B e G 1 4…”

Promising Materials and Synthesis Methods for Resistive Switching Memory Devices: A Status Review

“…Chougale et al fabricated a Cu/silk fibroin gel/Cu synaptic device and demonstrated its application in wearable technology . In their other work, Chougale et al have developed an ITO/GeO/Ag device and demonstrated its application in data storage and neuromorphic computing applications . Kook et al have used silk fibroin for the RS devices and suggested its use in wearable and implantable systems .…”

“…243 In their other work, Chougale et al have developed an ITO/GeO/Ag device and demonstrated its application in data storage and neuromorphic computing applications. 244 Kook et al have used silk fibroin for the RS devices and suggested its use in wearable and implantable systems. 245 Moreover, a chitosan-based RS device was studied for nonvolatile memory application.…”