{"title":"用于高性能ffet记忆和人工突触的具有增强极化的cmos兼容ScAlN铁电薄膜。","authors":"Bingqian Xu, Yao Cai, Zekai Wang, Qinwen Xu, Yuqi Ren, Xiang Chen, Chenxi Hu, Xiaohui Li, Jianping Shi, Chengliang Sun, Shishang Guo","doi":"10.1002/smtd.202500842","DOIUrl":null,"url":null,"abstract":"<p><p>ScAlN is an emerging nitride ferroelectric material that exhibits exceptional remnant polarization (P<sub>r</sub>) at ultrathin scales (<50 nm), stable single-phase ferroelectricity, and CMOS compatibility, making it highly promising for next-generation low-power, high-density memory and neuromorphic devices. However, ScAlN films deposited by conventional physical vapor deposition (PVD) faces challenges such as Sc precipitation and crystal orientation degradation at high Sc concentrations (>20%) and reduced thicknesses, leading to deteriorated ferroelectricity and increased leakage. In this work, it is demonstrated that an optimized substrate structure enables PVD-grown Sc<sub>0.2</sub>Al<sub>0.8</sub>N films to achieve significantly enhanced ferroelectric properties compared to conventional substrates, retaining high P<sub>r</sub> even at 20 nm thickness. This improvement is further validated with Sc<sub>0.3</sub>Al<sub>0.7</sub>N and Sc<sub>0.35</sub>Al<sub>0.65</sub>N films across varying thicknesses. Additionally, a Sc<sub>0.2</sub>Al<sub>0.8</sub>N-based FeFET fabricated on this substrate exhibits a 17 V memory window, >10<sup>3</sup> switching ratio, >10<sup>4</sup> s retention, and >10<sup>4</sup> cycle endurance. When configured as an artificial synapse, the device achieves 98.7% recognition accuracy in neural network training under encoded pulse voltages, highlighting its potential for energy-efficient computing.</p>","PeriodicalId":229,"journal":{"name":"Small Methods","volume":" ","pages":"e2500842"},"PeriodicalIF":10.7000,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CMOS-Compatible ScAlN Ferroelectric Thin Films with Enhanced Polarization for High-Performance FeFET Memory and Artificial Synapses.\",\"authors\":\"Bingqian Xu, Yao Cai, Zekai Wang, Qinwen Xu, Yuqi Ren, Xiang Chen, Chenxi Hu, Xiaohui Li, Jianping Shi, Chengliang Sun, Shishang Guo\",\"doi\":\"10.1002/smtd.202500842\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>ScAlN is an emerging nitride ferroelectric material that exhibits exceptional remnant polarization (P<sub>r</sub>) at ultrathin scales (<50 nm), stable single-phase ferroelectricity, and CMOS compatibility, making it highly promising for next-generation low-power, high-density memory and neuromorphic devices. However, ScAlN films deposited by conventional physical vapor deposition (PVD) faces challenges such as Sc precipitation and crystal orientation degradation at high Sc concentrations (>20%) and reduced thicknesses, leading to deteriorated ferroelectricity and increased leakage. In this work, it is demonstrated that an optimized substrate structure enables PVD-grown Sc<sub>0.2</sub>Al<sub>0.8</sub>N films to achieve significantly enhanced ferroelectric properties compared to conventional substrates, retaining high P<sub>r</sub> even at 20 nm thickness. This improvement is further validated with Sc<sub>0.3</sub>Al<sub>0.7</sub>N and Sc<sub>0.35</sub>Al<sub>0.65</sub>N films across varying thicknesses. Additionally, a Sc<sub>0.2</sub>Al<sub>0.8</sub>N-based FeFET fabricated on this substrate exhibits a 17 V memory window, >10<sup>3</sup> switching ratio, >10<sup>4</sup> s retention, and >10<sup>4</sup> cycle endurance. When configured as an artificial synapse, the device achieves 98.7% recognition accuracy in neural network training under encoded pulse voltages, highlighting its potential for energy-efficient computing.</p>\",\"PeriodicalId\":229,\"journal\":{\"name\":\"Small Methods\",\"volume\":\" \",\"pages\":\"e2500842\"},\"PeriodicalIF\":10.7000,\"publicationDate\":\"2025-06-19\",\"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.202500842\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Small Methods","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1002/smtd.202500842","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}
CMOS-Compatible ScAlN Ferroelectric Thin Films with Enhanced Polarization for High-Performance FeFET Memory and Artificial Synapses.
ScAlN is an emerging nitride ferroelectric material that exhibits exceptional remnant polarization (Pr) at ultrathin scales (<50 nm), stable single-phase ferroelectricity, and CMOS compatibility, making it highly promising for next-generation low-power, high-density memory and neuromorphic devices. However, ScAlN films deposited by conventional physical vapor deposition (PVD) faces challenges such as Sc precipitation and crystal orientation degradation at high Sc concentrations (>20%) and reduced thicknesses, leading to deteriorated ferroelectricity and increased leakage. In this work, it is demonstrated that an optimized substrate structure enables PVD-grown Sc0.2Al0.8N films to achieve significantly enhanced ferroelectric properties compared to conventional substrates, retaining high Pr even at 20 nm thickness. This improvement is further validated with Sc0.3Al0.7N and Sc0.35Al0.65N films across varying thicknesses. Additionally, a Sc0.2Al0.8N-based FeFET fabricated on this substrate exhibits a 17 V memory window, >103 switching ratio, >104 s retention, and >104 cycle endurance. When configured as an artificial synapse, the device achieves 98.7% recognition accuracy in neural network training under encoded pulse voltages, highlighting its potential for energy-efficient computing.
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.