Chengfeng Jiang , Jinwang Sun , Haiyan Chen , Lei Liu , Chuanchang Li , Dou Zhang
{"title":"通过调节沉积温度和薄膜厚度,优化了HZO3ZO12薄膜的储能性能","authors":"Chengfeng Jiang , Jinwang Sun , Haiyan Chen , Lei Liu , Chuanchang Li , Dou Zhang","doi":"10.1016/j.mtla.2025.102563","DOIUrl":null,"url":null,"abstract":"<div><div>Dielectric capacitors are critical for energy storage applications, especially in pulsed power systems, owing to their ultrahigh power density and ultrafast charge/discharge capabilities. Among them, HfO₂-based thin films are particularly promising for micro-energy storage devices. In this work, double-layered Hf₀.₅Zr₀.₅O₂(3 nm)/ZrO₂(12 nm) (HZO3ZO12) films are deposited across a wide temperature range (80–225 °C) to systematically investigate their energy storage performance. A machine learning-assisted multi-objective optimization approach is employed to identify the optimal deposition temperature, revealing 128 °C as the ideal condition for maximizing energy storage properties. Further thickness optimization based on this deposition temperature is used to enhance the performance, achieving an excellent energy storage density of 113 J/cm³ at an applied electric field of 9.1 MV/cm. This study demonstrates a powerful strategy combining machine learning with experimental design to optimize dielectric capacitors, providing a roadmap for developing high-performance energy storage materials.</div></div>","PeriodicalId":47623,"journal":{"name":"Materialia","volume":"44 ","pages":"Article 102563"},"PeriodicalIF":2.9000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimized energy storage performance in HZO3ZO12 thin films through modulation of deposition temperature and film thickness\",\"authors\":\"Chengfeng Jiang , Jinwang Sun , Haiyan Chen , Lei Liu , Chuanchang Li , Dou Zhang\",\"doi\":\"10.1016/j.mtla.2025.102563\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Dielectric capacitors are critical for energy storage applications, especially in pulsed power systems, owing to their ultrahigh power density and ultrafast charge/discharge capabilities. Among them, HfO₂-based thin films are particularly promising for micro-energy storage devices. In this work, double-layered Hf₀.₅Zr₀.₅O₂(3 nm)/ZrO₂(12 nm) (HZO3ZO12) films are deposited across a wide temperature range (80–225 °C) to systematically investigate their energy storage performance. A machine learning-assisted multi-objective optimization approach is employed to identify the optimal deposition temperature, revealing 128 °C as the ideal condition for maximizing energy storage properties. Further thickness optimization based on this deposition temperature is used to enhance the performance, achieving an excellent energy storage density of 113 J/cm³ at an applied electric field of 9.1 MV/cm. This study demonstrates a powerful strategy combining machine learning with experimental design to optimize dielectric capacitors, providing a roadmap for developing high-performance energy storage materials.</div></div>\",\"PeriodicalId\":47623,\"journal\":{\"name\":\"Materialia\",\"volume\":\"44 \",\"pages\":\"Article 102563\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materialia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2589152925002315\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materialia","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589152925002315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Optimized energy storage performance in HZO3ZO12 thin films through modulation of deposition temperature and film thickness
Dielectric capacitors are critical for energy storage applications, especially in pulsed power systems, owing to their ultrahigh power density and ultrafast charge/discharge capabilities. Among them, HfO₂-based thin films are particularly promising for micro-energy storage devices. In this work, double-layered Hf₀.₅Zr₀.₅O₂(3 nm)/ZrO₂(12 nm) (HZO3ZO12) films are deposited across a wide temperature range (80–225 °C) to systematically investigate their energy storage performance. A machine learning-assisted multi-objective optimization approach is employed to identify the optimal deposition temperature, revealing 128 °C as the ideal condition for maximizing energy storage properties. Further thickness optimization based on this deposition temperature is used to enhance the performance, achieving an excellent energy storage density of 113 J/cm³ at an applied electric field of 9.1 MV/cm. This study demonstrates a powerful strategy combining machine learning with experimental design to optimize dielectric capacitors, providing a roadmap for developing high-performance energy storage materials.
期刊介绍:
Materialia is a multidisciplinary journal of materials science and engineering that publishes original peer-reviewed research articles. Articles in Materialia advance the understanding of the relationship between processing, structure, property, and function of materials.
Materialia publishes full-length research articles, review articles, and letters (short communications). In addition to receiving direct submissions, Materialia also accepts transfers from Acta Materialia, Inc. partner journals. Materialia offers authors the choice to publish on an open access model (with author fee), or on a subscription model (with no author fee).