Qin Xie, Xinqiang Pan, Wenbo Luo, Yao Shuai, Yi Wang, Junde Tong, Zebin Zhao, Chuangui Wu, Wanli Zhang
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.
忆阻器被认为是神经形态计算硬件实施的关键设备。忆阻器的电导训练过程直接影响着神经形态计算的性能。然而,忆阻器击穿和电导衰减仍然阻碍着基于无源忆阻器的神经网络的精确训练过程。在这里,通过在带有氧空位(OVs)的单晶 LN 薄膜和铂层之间插入 AlOx 亚氧化物层,设计出了 AlOx/LiNbO3 (LN)忆阻器。在相同的训练条件下,AlOx/LN忆阻器的电导和自顺应电流效应较低。移除外部刺激后,电导会出现轻微的自发衰减。为了探索亚氧化铝层对防止器件击穿和抑制电导衰减的影响,我们通过飞行时间二次离子质谱仪(ToF-SIMS)揭示了有亚氧化铝层和无亚氧化铝层器件的忆阻器机理。我们有理由相信,忆阻器中的氧化铝插入层可以作为抑制器件击穿的自顺应电流层,并为抑制电导衰减提供 OV 储存。这些结果为实现更可靠、更精确的被动式忆阻器电导训练提供了新的可能性和理论依据。
{"title":"Effects of AlOx Sub‐Oxide Layer on Conductance Training of Passive Memristor for Neuromorphic Computing","authors":"Qin Xie, Xinqiang Pan, Wenbo Luo, Yao Shuai, Yi Wang, Junde Tong, Zebin Zhao, Chuangui Wu, Wanli Zhang","doi":"10.1002/aelm.202400651","DOIUrl":"https://doi.org/10.1002/aelm.202400651","url":null,"abstract":"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, AlO<jats:sub>x</jats:sub>/LiNbO<jats:sub>3</jats:sub> (LN) memristors are designed by inserting a AlO<jats:sub>x</jats:sub> 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 AlO<jats:sub>x</jats:sub>/LN memristor. Slight spontaneous decay of conductance is achieved after the removal of the external stimulation. To explore the effects of AlO<jats:sub>x</jats:sub> sub‐oxide layer on the prevention of device breakdown and suppression of conductance decay, the memristive mechanism of devices with and without AlO<jats:sub>x</jats:sub> layer is revealed via time‐of‐flight secondary ion mass spectrometer (ToF‐SIMS). It is reasonable to believe that the AlO<jats:sub>x</jats:sub> 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.","PeriodicalId":110,"journal":{"name":"Advanced Electronic Materials","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}