Centre for Functional Materials, Vellore Institute of Technology, Vellore, TN, 632014, India.
Department of Physics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, TN, 632014, India.
Sci Rep. 2023 May 9;13(1):7481. doi: 10.1038/s41598-023-33752-5.
The unprecedented need for data processing in the modern technological era has created opportunities in neuromorphic devices and computation. This is primarily due to the extensive parallel processing done in our human brain. Data processing and logical decision-making at the same physical location are an exciting aspect of neuromorphic computation. For this, establishing reliable resistive switching devices working at room temperature with ease of fabrication is important. Here, a reliable analog resistive switching device based on Au/NiO nanoparticles/Au is discussed. The application of positive and negative voltage pulses of constant amplitude results in enhancement and reduction of synaptic current, which is consistent with potentiation and depression, respectively. The change in the conductance resulting in such a process can be fitted well with double exponential growth and decay, respectively. Consistent potentiation and depression characteristics reveal that non-ideal voltage pulses can result in a linear dependence of potentiation and depression. Long-term potentiation (LTP) and Long-term depression (LTD) characteristics have been established, which are essential for mimicking the biological synaptic applications. The NiO nanoparticle-based devices can also be used for controlled synaptic enhancement by optimizing the electric pulses, displaying typical learning-forgetting-relearning characteristics.
在现代技术时代,对数据处理的空前需求为神经形态设备和计算带来了机遇。这主要是由于我们大脑中进行的广泛的并行处理。在同一物理位置进行数据处理和逻辑决策是神经形态计算的一个令人兴奋的方面。为此,建立可靠的室温下工作且易于制造的电阻开关器件非常重要。在这里,讨论了一种基于 Au/NiO 纳米粒子/Au 的可靠模拟电阻开关器件。施加恒定幅度的正、负电压脉冲会导致突触电流增强和减弱,分别与增强和抑制相对应。这种过程导致的电导变化可以很好地拟合双指数增长和衰减。一致的增强和抑制特性表明,非理想电压脉冲会导致增强和抑制呈线性关系。已经建立了长时程增强(LTP)和长时程抑制(LTD)特性,这对于模拟生物突触应用至关重要。通过优化电脉冲,基于 NiO 纳米粒子的器件还可以用于控制突触增强,显示出典型的学习-遗忘-再学习特征。