Designing high endurance Hf0.5Zr0.5O2 capacitors through engineered recovery from fatigue for non-volatile ferroelectric memory and neuromorphic hardware
{"title":"Designing high endurance Hf0.5Zr0.5O2 capacitors through engineered recovery from fatigue for non-volatile ferroelectric memory and neuromorphic hardware","authors":"Xinye Li, Padma Srivari, Sayani Majumdar","doi":"arxiv-2409.00635","DOIUrl":null,"url":null,"abstract":"Heavy computational demands from artificial intelligence (AI) leads the\nresearch community to explore the design space for functional materials that\ncan be used for high performance memory and neuromorphic computing hardware.\nNovel device technologies with specially engineered properties are under\nintense investigation to revolutionize information processing with\nbrain-inspired computing primitives for ultra energy-efficient implementation\nof AI and machine learning tasks. Ferroelectric memories with ultra-low power\nand fast operation, non-volatile data retention and reliable switching to\nmultiple polarization states promises one such option for non-volatile memory\nand synaptic weight elements in neuromorphic hardware. For quick adaptation of\nindustry, new materials need complementary metal oxide semiconductor (CMOS)\nprocess compatibility which brings a whole new set of challenges and\nopportunities for advanced materials design. In this work, we report on\ndesigning of back-end-of-line compatible ferroelectric Hf0.5Zr0.5O2 capacitors\nthat are capable of recovery from fatigue multiple times reaching 2Pr > 40\nmicroC cm-2 upon each retrieval. Our results indicate that with specifically\nengineered material stack and annealing protocols, it is possible to reach\nendurance exceeding 10^9 cycles at room temperature that can lead to ultralow\npower ferroelectric non-volatile memory components or synaptic weight elements\ncompatible with online training or inference tasks for neuromorphic computing.","PeriodicalId":501083,"journal":{"name":"arXiv - PHYS - Applied Physics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Applied Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.00635","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Heavy computational demands from artificial intelligence (AI) leads the
research community to explore the design space for functional materials that
can be used for high performance memory and neuromorphic computing hardware.
Novel device technologies with specially engineered properties are under
intense investigation to revolutionize information processing with
brain-inspired computing primitives for ultra energy-efficient implementation
of AI and machine learning tasks. Ferroelectric memories with ultra-low power
and fast operation, non-volatile data retention and reliable switching to
multiple polarization states promises one such option for non-volatile memory
and synaptic weight elements in neuromorphic hardware. For quick adaptation of
industry, new materials need complementary metal oxide semiconductor (CMOS)
process compatibility which brings a whole new set of challenges and
opportunities for advanced materials design. In this work, we report on
designing of back-end-of-line compatible ferroelectric Hf0.5Zr0.5O2 capacitors
that are capable of recovery from fatigue multiple times reaching 2Pr > 40
microC cm-2 upon each retrieval. Our results indicate that with specifically
engineered material stack and annealing protocols, it is possible to reach
endurance exceeding 10^9 cycles at room temperature that can lead to ultralow
power ferroelectric non-volatile memory components or synaptic weight elements
compatible with online training or inference tasks for neuromorphic computing.