Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of computing to overcome the von Neumann bottleneck. Electronic synaptic memristor devices which can compete with the biological synapses are indeed significant for neuromorphic computing. In this work, we demonstrate our efforts to develop and realize the graphene oxide (GO) based memristor device as a synaptic device, which mimic as a biological synapse. Indeed, this device exhibits the essential synaptic learning behavior including analog memory characteristics, potentiation and depression. Furthermore, spike-timing-dependent-plasticity learning rule is mimicked by engineering the pre- and post-synaptic spikes. In addition, non-volatile properties such as endurance, retentivity, multilevel switching of the device are explored. These results suggest that Ag/GO/fluorine-doped tin oxide memristor device would indeed be a potential candidate for future neuromorphic computing applications.
Bio‐resistive random access memory (Bio‐RRAM) devices are important because they are biocompatible and biodegradable. Herein, analog unipolar resistive switching and bipolar resistive switching in chitosan and reduced graphene oxide + chitosan (RGO+chitosan)‐based Bio‐RRAM devices are demonstrated, respectively. Endurance and retentivity of the Ag/RGO+chitosan/FTO‐based Bio‐RRAM device demonstrate good stability and nonvolatile in nature. Conductive atomic force microscope measurements infer the formation of the 1D conduction channels in the device, which further confirms the conduction channel mechanism in the RGO+chitosan‐based Bio‐RRAM device. Further, synaptic learning rules such as long‐term potentiation (LTP), long‐term depression, and spike time‐dependent plasticity on Ag/RGO+chitosan/FTO‐based device are also demonstrated. Estimated relaxation time infers more time for forgetting than learning. These results suggest that Ag/RGO+chitosan/FTO synaptic Bio‐RRAM device would indeed be a potential candidate for future neuromorphic computing applications.
We report on the α -Fe2O3-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-Fe2O3/FTO and exhibits non – volatility with analog resistive switching characteristics. We successfully demonstrated synaptic learning rules such as long-term potentiation (LTP), Long-term depression (LTD), and spike time-dependent plasticity (STDP). In addition, we also presented off-chip training to obtain good accuracy by backpropagation algorithm considering the synaptic weights obtained from α-Fe2O3 based artificial synaptic device. The proposed α -Fe2O3-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 artificial neural network (ANN) implementation.
This study reports on the temperature stability of the Ag/chitosan/fluorine‐doped tin oxide (Ag/chitosan/FTO)‐based bio‐resistive random‐access memory (bio‐RRAM) device through current–voltage (I–V) characteristics in the temperature range of 280–360 K. From I–V characteristics, it is affirmed that in the present device, the unipolar nature of resistive switching is highly stable and reproducible. The device is quite stable at 360 K. Activation energy is higher in the low resistance state (LRS) (≈0.096 eV) compared with the high resistance state (HRS) (≈0.076 eV) due to sufficient thermal energy to cross the barrier at high temperature. From 280 to 360 K, the conduction mechanism in the HRS of the chitosan device is followed by a direct tunneling mechanism, while the Schottky mechanism is dominated in the LRS. Barrier height calculated from Schottky mechanism in an LRS is found to increase with temperature from 0.50 eV (280 K) to 0.66 eV (360 K). Evidenced current values up to 200 pA obtained with a conducting atomic force microscope infer that conduction in the chitosan‐based device is due to filaments formed by oxygen defects. It is believed that the present results are helpful for the development of future bio‐RRAM devices.
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