Depth resolved lattice-charge coupling in epitaxial BiFeO3 thin film

Lee, Hyeon Jun; Lee, Sung Su; Kwak, Jin Ho; Kim, Young‐Min; Jeong, Hu Young; Borisevich, Albina Y.; Lee, Su Yong; Noh, Do Young; Kwon, Oh Heon; Kim, Yunseok; Jo, Ji Young

doi:10.1038/srep38724

Cited by 9 publications

(2 citation statements)

References 31 publications

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“…We have selected the results for the thin films grown on STO, LAO, and (LaAlO 3 ) 0.3 (Sr 2 TaAlO 6 ) 0.7 (LSAT) (001) substrates by PLD. [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] Despite the small size of the dataset, the following approach was found to be efficient in assessing the epitaxial strain relaxation and predicting h c .…”

Section: Dataset Constructionmentioning

confidence: 99%

Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation

Shin

Choi

2022

J Am Ceram Soc.

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Understanding epitaxial strain relaxation is one of the key challenges in functional thin films with strong structure–property relations. Herein, we employ an emerging data analytics approach to quantitatively evaluate the underlying relationships between critical thickness (hc) of strain relaxation and various physical and chemical features, despite the sporadic experimental data points available. First, we have collected and refined the reported hc of the perovskite oxide thin film/substrate system to construct a consistent sub‐dataset which captures a common trend among the varying experimental details. Then, we employ correlation analyses and feature engineering to find the most relevant feature set which includes Poisson's ratio and lattice mismatch. With the insight offered by correlation analyses and feature engineering, machine learning (ML) models have been trained to deduce a decent accuracy, which has been further validated experimentally. The demonstrated framework is expected to be efficiently extended to the other classes of thin films in understanding hc.

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Section: Dataset Constructionmentioning

confidence: 99%

Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation

Shin

Choi

2022

J Am Ceram Soc.

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“…Among these techniques, high resolution transmission and scanning electron microscopy (HRTEM & HRSTEM) [28,29], convergent beam electron diffraction (CBED) [30], nano-beam electron diffraction (NBED) [31,32], dark-field electron holography (DFEH) [29], STEM Moiré interference [33][34][35] and geometrical phase analysis (HRTEM-GPA) [18,36,37]. In contrast to semiconductors, few recent works have been reported on strain measurements using TEM based techniques in ferroic materials indicating that this field still remains an experimental challenge [3,38].…”

Section: Introductionmentioning

confidence: 99%

Quantification and mapping of elastic strains in ferroelectric [BaZrO3]xᴧ/[BaTiO3](1-x)ᴧ superlattices

Belhadi

Ravaux

Bouyanfif

et al. 2020

Applied Surface Science

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We report on quantification and elastic strain mapping in two artificial [BaZrO 3 ] xᴧ /[BaTiO 3 ] (1-x)ᴧ (BZ xᴧ /BT (1-x)ᴧ) superlattices having periods of Λ=6.6 nm and Λ = 11 nm respectively, grown on (001) SrTiO 3 single crystal substrate by pulsed laser deposition technique. The methodology consists of a combination of high-resolution scanning transmission electron microscopy and nanobeam electron diffraction associated with dedicated algorithm for diffraction patterns processing originally developed for semiconductors to record the strains at atomic scale. Both in-plane and out-of-plane elastic strains were then determined at 2 nm spatial resolution and their average values were used to map the strains along and transverse to the epitaxial growth direction of both samples to determine its variation along several BZ/BT interfaces. In addition, the variation of the width of the inter-diffusion BT/BZ interfaces and intermixing between different layers are estimated. The obtained width average value measured in these inter-diffusion interfaces vary from 8 to 12% and from 9 to 11% for both superlattices having Λ = 6.6 nm and Λ = 11 nm respectively. These inter-diffusion interfaces and the inherent elastic strains due to the confined layers of the superlattices are known to be the most important parameters, responsible of the change in their functional properties.

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Gate‐Induced Massive and Reversible Phase Transition of VO₂ Channels Using Solid‐State Proton Electrolytes

Lee

et al. 2018

Adv Funct Materials

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The use of gate bias to control electronic phases in VO 2 , an archetypical correlated oxide, offers a powerful method to probe their underlying physics, as well as for the potential to develop novel electronic devices. Up to date, purely electrostatic gating in 3-terminal devices with correlated channel shows the limited electrostatic gating efficiency due to insufficiently induced carrier density and short electrostatic screening length. Here massive and reversible conductance modulation is shown in a VO 2 channel by applying gate bias V G at low voltage by a solid-state proton (H + ) conductor. By using porous silica to modulate H + concentration in VO 2 , gate-induced reversible insulator-tometal (I-to-M) phase transition at low voltage, and unprecedented two-step insulator-to-metal-to-insulator (I-to-M-to-I) phase transition at high voltage are shown. V G strongly and efficiently injects H + into the VO 2 channel without creating oxygen deficiencies; this H + -induced electronic phase transition occurs by giant modulation (≈7%) of out-of-plane lattice parameters as a result of H + -induced chemical expansion. The results clarify the role of H + on the electronic state of the correlated phases, and demonstrate the potentials for electronic devices that use ionic/electronic coupling.

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Depth resolved lattice-charge coupling in epitaxial BiFeO3 thin film

Cited by 9 publications

References 31 publications

Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation

Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation

Quantification and mapping of elastic strains in ferroelectric [BaZrO3]xᴧ/[BaTiO3](1-x)ᴧ superlattices

Gate‐Induced Massive and Reversible Phase Transition of VO₂ Channels Using Solid‐State Proton Electrolytes

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Depth resolved lattice-charge coupling in epitaxial BiFeO3 thin film

Cited by 9 publications

References 31 publications

Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation

Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation

Quantification and mapping of elastic strains in ferroelectric [BaZrO3]xᴧ/[BaTiO3](1-x)ᴧ superlattices

Gate‐Induced Massive and Reversible Phase Transition of VO2 Channels Using Solid‐State Proton Electrolytes

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Gate‐Induced Massive and Reversible Phase Transition of VO₂ Channels Using Solid‐State Proton Electrolytes