Memristors are recognized as crucial devices for the hardware implementation of neuromorphic computing. The conductance training process of memristors has a direct impact on the performance of neuromorphic computing. However, memristor breakdown and conductance decay still hinder the precise training process of neural networks based on passive memristor. Here, AlOx/LiNbO3 (LN) memristors are designed by inserting a AlOx sub‐oxide layer between the single‐crystalline LN thin film with oxygen vacancies (OVs) and Pt layer. Under the same training conditions, lower conductance and self‐compliance current effects are observed in AlOx/LN memristor. Slight spontaneous decay of conductance is achieved after the removal of the external stimulation. To explore the effects of AlOx sub‐oxide layer on the prevention of device breakdown and suppression of conductance decay, the memristive mechanism of devices with and without AlOx layer is revealed via time‐of‐flight secondary ion mass spectrometer (ToF‐SIMS). It is reasonable to believe that the AlOx inserting layer in memristors can serve as a self‐compliance current layer to inhibit device breakdown and provide the OVs reservoir to suppress conductance decay. These results offer new possibilities and theoretical grounds for achieving more reliable and precise conductance training of passive memristors.

中文翻译：

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AlOx 亚氧化物层对神经形态计算被动忆阻器电导训练的影响


忆阻器被认为是神经形态计算硬件实现的关键设备。忆阻器的电导训练过程对神经形态计算的性能有直接影响。然而，忆阻器击穿和电导衰减仍然阻碍了基于无源忆阻器的神经网络的精确训练过程。在这里，AlOx/LiNbO3 （LN） 忆阻器是通过在具有氧空位 （OV） 的单晶 LN 薄膜和 Pt 层之间插入 AlOx 亚氧化物层来设计的。在相同的训练条件下，在 AlOx/LN 忆阻器中观察到较低的电导和自顺应电流效应。去除外部刺激后，电导轻微自发衰减。为了探索 AlOx 亚氧化物层对防止器件击穿和抑制电导衰减的影响，通过飞行时间二次离子质谱仪 （ToF-SIMS） 揭示了带和不带 AlOx 层的器件的忆阻机制。有理由相信，忆阻器中的 AlOx 插入层可以用作自顺应电流层来抑制器件击穿并提供 OVs 储液槽以抑制电导衰减。这些结果为实现无源忆阻器的更可靠和精确的电导训练提供了新的可能性和理论基础。