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

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.