journal article·2022-01

Effect of residual impurities on polarization switching kinetics in atomic-layer-deposited ferroelectric Hf0.5Zr0.5O2 thin films

Dong Hyun Lee, Geun Taek Yu, Ju Yong Park, Se Hyun Kim, Kun Yang, Geun Hyeong Park, Jin Ju Ryu, Je In Lee, Gun Hwan Kim, Min Hyuk Park

Acta Materialia· Volume 222
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Abstract

Abstract The ferroelectricity in fluorite-structured ferroelectrics such as HfO2 and ZrO2 has attracted increasing interest in both academia and industry. Although the polarization switching kinetics is one of the research topics which critically requires further study for the novel ferroelectric material, various factors affecting the polarization switching kinetics have not been rarely examined to date. Especially in atomic-layer-deposited films, the defects including residual impurities are expected to strongly influence the polarization switching kinetics. In this study, therefore, the effect of deposition temperature (Tdep) and impurity concentration on the physical/electrical properties and polarization switching kinetics of atomic-layer-deposited Hf0.5Zr0.5O2 thin films is reported based on physical/chemical/electrical analyses. With decreasing Tdep, the concentration of residual impurities, including carbon increased, resulting in decreased grain size. The increased defect concentration decreased the median switching time and increased the deviation of the switching time distribution. This can be attributed to the lowered energy barrier for the nucleation of oppositely polarized domains and increased spatial inhomogeneities, respectively. This result suggests that Tdep is a key process parameter for optimizing the polarization reversal behavior of fluorite-structured ferroelectrics as it has a considerable influence on the ferroelectric properties and polarization switching kinetics, which are strongly correlated to the defect concentrations and microstructure.
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Research context

OpenAlex topics: Ferroelectric and Negative Capacitance Devices · MXene and MAX Phase Materials · Advanced Memory and Neural Computing

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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