论文标题
扩散的回忆性滞后和人造神经元尖峰的温度控制
Temperature control of diffusive memristor hysteresis and artificial neuron spiking
论文作者
论文摘要
回忆设备是节能神经形态计算和未来人工智能系统的有希望的要素。对于扩散的备再次出现，由于Ag纳米颗粒在介电矩阵中漂移和扩散而导致设备终端之间的传导柱的顺序形成和传导柱消失。此过程受电压在设备触点上的应用。在这里，在实验和理论上，我们都证明了变化的温度可有效控制该设备中的备忘录状态和电荷运输。我们发现，通过升高和降低设备温度，可以重置回忆录状态，并在恒定施加的电压下在回忆录电路中产生当前的尖峰时，更改备忘录的剩余时间保持在高电阻状态和低电阻状态。我们的理论模型与实验表现出了良好的定性一致性，并有助于解释报告的效果。
Memristive devices are promising elements for energy-efficient neuromorphic computing and future artificial intelligence systems. For diffusive memristors, the device state switching occurs because of the sequential formation and disappearance of conduction pillars between device terminals due to the drift and diffusion of Ag nanoparticles in the dielectric matrix. This process is governed by the application of the voltage to the device contacts. Here, both in experiment and in theory we demonstrate that varying temperature offers an efficient control of memristor states and charges transport in the device. We found out that by raising and lowering the device temperature, one can reset the memristor state as well as change the residual time the memristor stays in high and low resistive states when the current spiking is generated in the memristive circuit at a constant applied voltage. Our theoretical model demonstrates a good qualitative agreement with the experiments and helps to explain the effects reported.
