superior to conventional computers as it is highly energy efficient, fault tolerant and has parallel processing capabilities, all this in such a small volume [2,3]. Advancements in both neuroscience and electronics have led to the area of neuromorphic computing, in which attempts are being made to incorporate certain aspects of neurobiological anatomy into the design and implementation of computing systems by implementing hardware neurons and synapses [4]. In a biological system,
In this work, we introduce a new class of p-type transparent conductive oxide (TCO) CuCrO 2 (150 nm) heterogeneously integrated onto FTO/glass for forming free memristor based neuromorphic applications. The fabricated Al/CuCrO 2 /FTO memristors demonstrate a reliable bipolar resistive switching with an ON/OFF ratio of 1000. The retention of the device was found to be steady even beyond 10 6 s, which demonstrates its non-volatility. The current-voltage (I-V) characteristics were fitted to evaluate its transport properties and a band-diagram was projected to have a better insight of the device operational principles. To validate the experimental observations, a new model has been developed, and the simulated I-V behavior was analogues to the experimental one. Efforts were then devoted to observe long-term potentiation (LTP) and longterm depression (LTD) utilizing identical but opposite pulses to evaluate the device's efficacy for synaptic applications. The synaptic behavior was well controlled by the pulse (pulse amplitude and width) variations. The conductance change was found to be symmetric and then saturated, which reflects the popular biological Hebbian rules. Finally, a long-term synaptic modulation has been implemented by establishing the spike rate dependent plasticity (SRDP) rule, which is a part of spiking neural networks and advantageous to mimic the brain's capability at low power. All the obtained experimental results were systematically corroborated by neural network simulation. Overall, our approach provides a new road map towards the development of TCO based alternative memristors, which can be employed to mimic the synaptic plasticity for energy-efficient bioinspired neuromorphic systems and non-Von Neumann computer architectures.
Purpose: Lubricants prepared with metal oxide nanoparticle additives are found to havebetter tribological properties. In this work the widely used commercial SN lubricating oil isadded with SiO2 nanoparticle and the tribological characteristics are studied. Wear testswere conducted on plain oil and oil with nanosized silica (SiO2) additives using a four balltribo-tester.Design/methodology/approach: The wear images of the ball specimens were analyzedusing confocal microscope to measure the topography of the wear parameters such as wearscar diameter, depth of wear, angle of the wear.Findings: It is found that the gear oil with silica nanoparticles is found to have improvedproperties. Results show a considerable reduction in the values of wear scar diameter, wearangle and wear depth for the gear oil added with SiO2 nanoparticles of 0.4 wt. %.Originality/value: In the work carried out, a new innovative technological approach toanalyze the tribological properties of lubricating oil by finding the wear scar diameter, wearangle and wear depth is reported.
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