Ab initio study of doping effects on the ferroelectric and piezoelectric properties of ZrO₂
Source abstract
Abstract
Ferroelectric binary oxides show great promise for application in energy storage, non-volatile memories, and neuromorphic computing devices. Compared to hafnia (HfO 2 ), zirconia (ZrO 2 ) thin films have been understudied in spite of significant recent progress. Doping has emerged as a promising tool to tune the ferroelectric properties of HfO 2 and Hf x Zr 1-x O 2 (HZO). However, an extensive study of the effects of doping on the structural and ferroelectric properties of ZrO 2 is still widely missing. In this study, through density functional theory (DFT) and ab initio molecular dynamics (AIMD) calculations we investigate the effects of B, Si, Al, Mg, Sc, Ca, Ce, Ta, Hf, Y, Sr, La, Ba and Rb doping on the structural distortions, relative phase stability, phase transition temperatures, spontaneous polarization and piezoelectric response of the monoclinic (m-), polar orthorhombic (o-) and tetragonal (t-) phases of ZrO 2 at 3.125 and 6.25 cat% doping concentration. We demonstrate that the spontaneous polarization magnitude in ZrO₂ can be significantly enhanced through doping with Si, B, and Al. In addition, our results reveal that the relative phase stability and phase transition temperatures of ZrO₂ can be tuned by doping. Specifically, our calculations confirm that Si doping promotes the stabilization of the t-phase in ZrO₂. Furthermore, we show that doping shifts the phase transition temperature, leading to substantial improvements in piezoelectric response, with up to a sevenfold increase observed in Al-doped ZrO₂. Consequently, this work highlights the most promising dopants capable of enhancing the ferroelectric and piezoelectric properties of ZrO₂ thin films.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ferroelectric and Negative Capacitance Devices · Ferroelectric and Piezoelectric Materials · Advanced Memory and Neural Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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