Bipolar resistive switching (RS) and synaptic behaviors of resistive random access memory (RRAM) based on TiO _x are demonstrated. RS uniformity is improved by introducing nitrogen into the RS layer using radio frequency sputtering in the reactive Ar/N₂ ambient. The conductive mechanism is in good agreement with the space−charge-limited conduction model. The activation energy fit by the Arrhenius equation and conductive atomic force microscopy results indicate that the conductive filaments are formed by oxygen vacancies. More importantly, reliable multilevel RRAM can be achieved by tuning the compliance current, which enables the achievement of distinguishable resistance states. Furthermore, multilevel RRAM enables the simulation of synaptic functions, such as learning−forgetting−relearning, habituation, and spike-timing-dependent plasticity (STDP). Image pattern recognition based on STDP learning rules using a digital memristor is demonstrated. The findings may offer a route to the development of future storage and neuromorphic computing.

中文翻译：

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用于信息存储和神经形态计算的基于 TiO x 掺杂 N 忆阻器的可靠电阻开关和突触行为

展示了基于 TiO _x的电阻式随机存取存储器 (RRAM) 的双极电阻开关 (RS) 和突触行为。通过在反应性 Ar/N _{2 中}使用射频溅射将氮引入 RS 层来提高 RS 均匀性 周围。传导机制与空间电荷限制传导模型非常吻合。由阿伦尼乌斯方程拟合的活化能和导电原子力显微镜结果表明导电细丝是由氧空位形成的。更重要的是，可以通过调整顺从电流来实现可靠的多级 RRAM，从而实现可区分的电阻状态。此外，多级 RRAM 能够模拟突触功能，例如学习-遗忘-再学习、习惯化和尖峰时间依赖性可塑性 (STDP)。演示了使用数字忆阻器基于 STDP 学习规则的图像模式识别。这些发现可能为未来存储和神经形态计算的发展提供一条途径。