Effect of Precursor Purge Time on Plasma-Enhanced Atomic Layer Deposition-Prepared Ferroelectric Hf<sub>0.5</sub>Zr<sub>0.5</sub>O<sub>2</sub> Phase and Performance
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Hafnium oxide-based thin films, in particular hafnium zirconium oxide (HZO), have potential for applications in nonvolatile memory and energy harvesting. Atomic layer deposition (ALD) is the most widely used method for HZO deposition due to its precise thickness control and ability to provide conformal coverage. Previous studies have shown the effects of different metal precursors, oxidizer precursors, and process temperatures on the ferroelectric properties of HZO. However, no mechanism has been identified to describe the different phase stabilities as the metal precursor purge time varies. This study investigates how varying the metal precursor purge time during plasma-enhanced ALD (PE-ALD) influences the phases and properties of the HZO thin films. Grazing incidence X-ray diffraction, Fourier transform infrared spectroscopy, and scanning transmission electron microscopy are used to study the changes in phase of HZO with variation of the metal precursor purge time during the PE-ALD process. The phases observed are correlated with polarization and relative permittivity responses under an electric field, including wake-up and endurance effects. The resulting phases and properties are linked to changes in composition, as measured using time-of-flight secondary ion mass spectrometry and X-ray photoelectron spectroscopy. It is shown that short metal precursor purge times result in increased carbon and nitrogen impurities and stabilization of the antipolar Pbca phase. Long purge times lead to films comprising predominantly the ferroelectric Pca2 1 phase.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".