Planarizing Spalled GaAs(100) Surfaces by MOVPE Growth
Bibliographic record
Abstract
III–V photovoltaic devices have demonstrated exceptional performance across various applications, with controlled crystal fracturing, known as controlled spalling, emerging as a promising method to reduce costs by enabling substrate reuse. Spalling GaAs(100) substrates, a commonly used substrate in III–V photovoltaics, results in faceted ridges that must be planarized to grow high-quality photovoltaic devices. Here we demonstrate that a GaAs(100) wafer offcut toward [01̅1] and spalled toward [011] can be efficiently planarized by growing C:GaAs by metal–organic vapor phase epitaxy (MOVPE) on the surface, with up to 95% of the nominally deposited material used to fill the valleys between ridges. We find that reducing the offcut to 2° enhances the planarizing capability of C:GaAs. A surface morphology model indicates that the density of surface dangling bonds significantly influences the growth evolution of undoped GaAs surfaces. In contrast, the model suggests that the effectiveness of C:GaAs as a smoothing layer stems from modifying the atomic surface structure and, consequently, the associated sticking coefficients of the facets, which can alter the evolution of surface morphology. Our findings provide guidelines for the epitaxial planarization of semiconductor surfaces and improve the understanding of MOVPE growth on nonplanar surfaces.
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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 | 0.000 |
| 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".