{"title":"利用ZrO2-x氧空位储层增强hfo2基三层记忆电阻器的突触特性","authors":"Turgun Boynazarov, Joonbong Lee, Hojin Lee, Sangwoo Lee, Hyunbin Chung, Dae Haa Ryu, Haider Abbas, Taekjib Choi","doi":"10.1016/j.jmst.2024.12.020","DOIUrl":null,"url":null,"abstract":"Neuromorphic computing devices leveraging HfO<sub>2</sub> and ZrO<sub>2</sub> materials have recently garnered significant attention due to their potential for brain-inspired computing systems. In this study, we present a novel trilayer Pt/HfO<sub>2</sub>/ZrO<sub>2-</sub><em><sub>x</sub></em>/HfO<sub>2</sub>/TiN memristor, engineered with a ZrO<sub>2-</sub><em><sub>x</sub></em> oxygen vacancy reservoir (OVR) layer fabricated via radio frequency (RF) sputtering under controlled oxygen ambient. The incorporation of the ZrO<sub>2-</sub><em><sub>x</sub></em> OVR layer enables enhanced resistive switching characteristics, including a high ON/OFF ratio (∼8000), excellent uniformity, robust data retention (>10⁵ s), and multilevel storage capabilities. Furthermore, the memristor demonstrates superior synaptic plasticity with linear long-term potentiation (LTP) and depression (LTD), achieving low non-linearity values of 1.36 (LTP) and 0.66 (LTD), and a recognition accuracy of 95.3% in an MNIST dataset simulation. The unique properties of the ZrO<sub>2-</sub><em><sub>x</sub></em> layer, particularly its ability to act as a dynamic oxygen vacancy reservoir, significantly enhance synaptic performance by stabilizing oxygen vacancy migration. These findings establish the OVR-trilayer memristor as a promising candidate for future neuromorphic computing and high-performance memory applications.","PeriodicalId":16154,"journal":{"name":"Journal of Materials Science & Technology","volume":"7 1","pages":""},"PeriodicalIF":11.2000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhanced synaptic properties in HfO2-based trilayer memristor by using ZrO2-x oxygen vacancy reservoir layer for neuromorphic computing\",\"authors\":\"Turgun Boynazarov, Joonbong Lee, Hojin Lee, Sangwoo Lee, Hyunbin Chung, Dae Haa Ryu, Haider Abbas, Taekjib Choi\",\"doi\":\"10.1016/j.jmst.2024.12.020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neuromorphic computing devices leveraging HfO<sub>2</sub> and ZrO<sub>2</sub> materials have recently garnered significant attention due to their potential for brain-inspired computing systems. In this study, we present a novel trilayer Pt/HfO<sub>2</sub>/ZrO<sub>2-</sub><em><sub>x</sub></em>/HfO<sub>2</sub>/TiN memristor, engineered with a ZrO<sub>2-</sub><em><sub>x</sub></em> oxygen vacancy reservoir (OVR) layer fabricated via radio frequency (RF) sputtering under controlled oxygen ambient. The incorporation of the ZrO<sub>2-</sub><em><sub>x</sub></em> OVR layer enables enhanced resistive switching characteristics, including a high ON/OFF ratio (∼8000), excellent uniformity, robust data retention (>10⁵ s), and multilevel storage capabilities. Furthermore, the memristor demonstrates superior synaptic plasticity with linear long-term potentiation (LTP) and depression (LTD), achieving low non-linearity values of 1.36 (LTP) and 0.66 (LTD), and a recognition accuracy of 95.3% in an MNIST dataset simulation. The unique properties of the ZrO<sub>2-</sub><em><sub>x</sub></em> layer, particularly its ability to act as a dynamic oxygen vacancy reservoir, significantly enhance synaptic performance by stabilizing oxygen vacancy migration. These findings establish the OVR-trilayer memristor as a promising candidate for future neuromorphic computing and high-performance memory applications.\",\"PeriodicalId\":16154,\"journal\":{\"name\":\"Journal of Materials Science & Technology\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":11.2000,\"publicationDate\":\"2025-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Materials Science & Technology\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jmst.2024.12.020\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Materials Science & Technology","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1016/j.jmst.2024.12.020","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Enhanced synaptic properties in HfO2-based trilayer memristor by using ZrO2-x oxygen vacancy reservoir layer for neuromorphic computing
Neuromorphic computing devices leveraging HfO2 and ZrO2 materials have recently garnered significant attention due to their potential for brain-inspired computing systems. In this study, we present a novel trilayer Pt/HfO2/ZrO2-x/HfO2/TiN memristor, engineered with a ZrO2-x oxygen vacancy reservoir (OVR) layer fabricated via radio frequency (RF) sputtering under controlled oxygen ambient. The incorporation of the ZrO2-x OVR layer enables enhanced resistive switching characteristics, including a high ON/OFF ratio (∼8000), excellent uniformity, robust data retention (>10⁵ s), and multilevel storage capabilities. Furthermore, the memristor demonstrates superior synaptic plasticity with linear long-term potentiation (LTP) and depression (LTD), achieving low non-linearity values of 1.36 (LTP) and 0.66 (LTD), and a recognition accuracy of 95.3% in an MNIST dataset simulation. The unique properties of the ZrO2-x layer, particularly its ability to act as a dynamic oxygen vacancy reservoir, significantly enhance synaptic performance by stabilizing oxygen vacancy migration. These findings establish the OVR-trilayer memristor as a promising candidate for future neuromorphic computing and high-performance memory applications.
期刊介绍:
Journal of Materials Science & Technology strives to promote global collaboration in the field of materials science and technology. It primarily publishes original research papers, invited review articles, letters, research notes, and summaries of scientific achievements. The journal covers a wide range of materials science and technology topics, including metallic materials, inorganic nonmetallic materials, and composite materials.