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

Xinye Li, Padma Srivari, Sayani Majumdar
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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.
通过疲劳恢复工程设计高耐久性 Hf0.5Zr0.5O2 电容器,用于非易失性铁电存储器和神经形态硬件
人工智能(AI)对计算的大量需求促使研究界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在接受深入研究,以便利用大脑启发的计算基元彻底改变信息处理方式,从而超节能地执行人工智能和机器学习任务。铁电存储器具有超低功耗、快速运行、非易失性数据保留和可靠的多极化状态切换等特性,有望成为神经形态硬件中非易失性存储器和突触权重元件的选择之一。为了快速适应行业发展,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铁电 Hf0.5Zr0.5O2 电容器的设计,这种电容器能够多次从疲劳中恢复,每次恢复时 2Pr > 40microC cm-2。我们的研究结果表明,通过专门设计的材料堆栈和退火协议,有可能在室温下达到超过 10^9 次循环的耐久性,从而生产出超低功率的铁电非挥发性存储器元件或突触权重元件,可用于神经形态计算的在线训练或推理任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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