标题： 通过疲劳恢复工程设计用于非易失性铁电存储器和神经形态硬件的高耐久性Hf0.5Zr0.5O2电容器 显示英文标题

标题： Designing high endurance Hf0.5Zr0.5O2 capacitors through engineered recovery from fatigue for non-volatile ferroelectric memory and neuromorphic hardware

摘要： 人工智能（AI）带来的巨大计算需求促使研究社区探索可用于高性能存储器和神经形态计算硬件的功能材料的设计空间。 具有特殊工程特性的新型器件技术正受到密切关注，旨在通过类脑计算原语实现AI和机器学习任务的超低功耗信息处理。 具有超低功耗、快速操作、非易失性数据保留和可靠切换到多种极化状态的铁电存储器，为神经形态硬件中的非易失性存储器和突触权重元件提供了一种选择。 为了快速适应工业应用，新材料需要与互补金属氧化物半导体（CMOS）工艺兼容，这为先进材料设计带来了全新的挑战和机遇。 在这项工作中，我们报告了后端兼容的Hf0.5Zr0.5O2铁电电容器的设计，这些电容器能够在每次检索时多次从疲劳中恢复，达到2Pr > 40微库仑每平方厘米。 我们的结果显示，通过专门设计的材料堆栈和退火方案，在室温下可以达到超过10^9次循环的耐久性，从而实现与神经形态计算在线训练或推理任务兼容的超低功耗铁电非易失性存储组件或突触权重元件。 显示英文摘要

摘要： 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.