Ferroelectric/dielectric composite tunnel junctions: influence of the stacking sequence on their microstructure
Bibliographic record
Abstract
Increasing the tunnel electroresistance of ferroelectric tunnel junctions can be achieved by replacing the single ferroelectric barrier by a ferroelectric/dielectric bilayer. For a given thickness of the layers, the stacking sequence (ferroelectric/dielectric or dielectric/ferroelectric) can lead to different resistivity. In this paper, we study composite tunnel junctions based on the Mn‐BiFeO3/SrTiO3 bilayer, deposited on a LaSrMnO3 (LSMO) buffer, grown on (001)‐oriented SrTiO3 (STO) substrates (fig. 1). The samples were grown by PLD. The nominal thicknesses are 0.8nm for SrTiO3 and 2.8 nm for Mn‐BiFeO3 (BFO). TEM/HRTEM micrographs reveal the homogeneity of the bilayers. The sharpness and roughness of the interfaces are then studied by mean of Cs‐corrected HAADF‐STEM for both stacking sequences. STO/BFO/LSMO (fig. 1a): for this configuration, both BFO/LSMO and STO/BFO interfaces appear flat, suggesting a homogeneous thickness of the BFO layer of 2.8 nm. The top surface of STO exhibits steps of half perovskite‐cell height leading to an effective STO thickness of about three to four unit‐cell; somewhat thicker than the awaited one. BFO/STO/LSMO (fig. 1b): whereas the STO/LSMO interface looks flat and sharp, the BFO/STO interface exhibits a roughness of the order of one perovskite unit‐cell, indicating that the effective STO layer thickness is somewhat inhomogeneous, but close to the awaited 0.8nm thickness. The BFO surface looks flat, suggesting that the effective BFO thickness varies along the bilayer but also remains close to the 2.8nm awaited thickness. Deeper insights on the interfaces sharpness are further obtained by ELNES measurements at the Mn L23 , Fe L23 , Ti L23 and O‐K edges. Peculiar fingerprints of the Fe and Ti L23‐edges are observed at the bilayer interfaces suggesting slight changes of the crystal field in their vicinity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".