{"title":"Oxygen-Vacancy-Engineered Y<sub>2</sub>O<sub>3</sub>/CeO<sub>2</sub> Nanobrush Superlattices via Laser Heteroepitaxy: Toward High-Performance Memristors.","authors":"Shuowen Zhang, Ling Wu, Pengbo Wang, Yifeng Lv, Qiwei Song, Jinzhong Lu, Huaping Wu, Lisha Fan, Jianhua Yao","doi":"10.1002/smtd.202502421","DOIUrl":null,"url":null,"abstract":"<p><p>While oxide-based memristors enable CMOS-compatible in-memory computing, their confinement to single-material memristive layers restricts heterogeneous interfacial engineering-limiting neuromorphic adaptability. Here, a unique nanobrush-based Y<sub>2</sub>O<sub>3</sub>/CeO<sub>2</sub> superlattice structure with an average height of 75 nm is fabricated using diffusion-limited laser epitaxy. Atomic-resolution structural characterization reveals that each Y<sub>2</sub>O<sub>3</sub>/CeO<sub>2</sub> superlattice nanobrush exhibits self-organized heterogeneous interfaces along (111) crystallographic orientation. Notably, the nanobrush memristor exhibits unipolar switching with a R<sub>ON</sub>/R<sub>OFF</sub> ratio ∼12 higher than film-based superlattice devices, plus excellent endurance (1000 cycles) and retention (10<sup>4</sup> s). The enhanced resistive switching in nanobrush memristors likely stems from their brush geometry and space charge enrichment at chevron-like heterointerfaces. XPS analysis confirms abundant oxygen vacancies in the nanobrush-based superlattices, generating substantial mobile ionic defects. The vertically-aligned nanobrush geometry provides abundant conduction pathways for oxygen vacancy migration. Ultimately, these fluorite-bixbyite superlattice nanobrushes demonstrate structurally engineered, energy-efficient, noise-resistant memory for high-density neuromorphic circuits.</p>","PeriodicalId":229,"journal":{"name":"Small Methods","volume":" ","pages":"e02421"},"PeriodicalIF":9.1000,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Small Methods","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/smtd.202502421","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
While oxide-based memristors enable CMOS-compatible in-memory computing, their confinement to single-material memristive layers restricts heterogeneous interfacial engineering-limiting neuromorphic adaptability. Here, a unique nanobrush-based Y2O3/CeO2 superlattice structure with an average height of 75 nm is fabricated using diffusion-limited laser epitaxy. Atomic-resolution structural characterization reveals that each Y2O3/CeO2 superlattice nanobrush exhibits self-organized heterogeneous interfaces along (111) crystallographic orientation. Notably, the nanobrush memristor exhibits unipolar switching with a RON/ROFF ratio ∼12 higher than film-based superlattice devices, plus excellent endurance (1000 cycles) and retention (104 s). The enhanced resistive switching in nanobrush memristors likely stems from their brush geometry and space charge enrichment at chevron-like heterointerfaces. XPS analysis confirms abundant oxygen vacancies in the nanobrush-based superlattices, generating substantial mobile ionic defects. The vertically-aligned nanobrush geometry provides abundant conduction pathways for oxygen vacancy migration. Ultimately, these fluorite-bixbyite superlattice nanobrushes demonstrate structurally engineered, energy-efficient, noise-resistant memory for high-density neuromorphic circuits.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.