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 × 10⁴ 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.

由于硬件和软件能力的快速增长，包括人工智能（AI）和机器学习在内的类脑神经形态计算的发展非常重要，可以有效地处理大数据。用于神经形态计算的设备必须满足多级状态、高运行速度、低能耗以及足够的耐用性、保持性和线性度等基本要求。在这项研究中，利用在室温下合成的无机钙钛矿型非晶钒酸锶（a-SrVO _x : a-SVO）来生产高性能忆阻器，该忆阻器具有非易失性多级电阻开关和突触特性。电气特性分析表明 a-SVO 忆阻器具有典型的双极电阻开关行为。在关到开和开到关的转换过程中也观察到多级电阻状态。 a-SVO 忆阻器的保持电阻在 2 × 10 ⁴ s 的时间内没有显着变化。 Ag/a-SVO/Pt 忆阻器内运行的传导机制归因于银基细丝的形成。还进行非线性神经网络模拟来评估突触行为。这些结果表明，基于 a-SVO 的忆阻器在高性能神经形态计算设备中具有广阔的应用前景。