Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing

Abstract

The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and software capacities, which allows for the efficient handling of big data. Devices for neuromorphic computing must satisfy basic requirements such as multilevel states, high operating speeds, low energy consumption, and sufficient endurance, retention and linearity. In this study, inorganic perovskite-type amorphous strontium vanadate (a-SrVO x : a-SVO) synthesized at room temperature is utilized to produce a high-performance memristor that demonstrates nonvolatile multilevel resistive switching and synaptic characteristics. Analysis of the electrical characteristics indicates that the a-SVO memristor illustrates typical bipolar resistive switching behavior. Multilevel resistance states are also observed in the off-to-on and on-to-off transition processes. The retention resistance of the a-SVO memristor is shown to not significantly change for a period of 2 × 10 4 s. The conduction mechanism operating within the Ag/a-SVO/Pt memristor is ascribed to the formation of Ag-based filaments. Nonlinear neural network simulations are also conducted to evaluate the synaptic behavior. These results demonstrate that a-SVo-based memristors hold great promise for use in high-performance neuromorphic computing devices. open Scientific RepoRtS | (2020) 10:5761 | https://doi.

Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

“…1a). To our best knowledge, the two main features of analogue devices-the linear switching performance and the large dynamic range of Ti 4.8% :a-Si device-display a clear deviation from the previously reported Ag 9,10,15,17,18,[33][34][35][36][37][38][39][40][41][42][43] -and Cu 44,45 -based analogue CBRAM (Fig. 2c).…”

Amylum Resistive Random Access Memory Based on the Quantization Effect of Gold Nanoparticles

“…Research on this device involves multiple disciplines and fields, such as materials, chemistry, physics, and biology. Its application fields include data storage, 11,12 neuropsychological calculation, 13,14 logic operations, 15 pattern recognition, 16−18 communication encryption, and brain chips. 19 In recent years, great progress has been made in the research of nanomaterials as active layers of RRAM, such as zerodimensional materials (Si, 20 quantum dots, 21 etc.…”

Asymmetric GaN/ZnO Engineered Resistive Memory Device for Electronic Synapses

“…GaN is one of the most promising materials for power electronics with high electron mobility and a band gap of ∼3.3 eV, which can help to improve switching frequency, system volume, and energy conversion efficiency in electronic devices. − For this work, we have used the sputtering technique for the deposition of the ZnO and GaN thin film. The sputtering is used to make memristors due to its simplicity, low cost, and large-scale deposition . We easily control the morphology, crystallinity, thickness, and surface roughness by tuning the sputtering parameters such as the power, pressure, temperature, and gas ratio.…”

“…The sputtering is used to make memristors due to its simplicity, low cost, and large-scale deposition. 39 We easily control the morphology, crystallinity, thickness, and surface roughness by tuning the sputtering parameters such as the power, pressure, temperature, and gas ratio.…”