Elastic strain relaxation in InGaN nanopillars on nanoporous GaN
Bibliographic record
Abstract
InGaN-based devices are pivotal in the lighting market due to their high efficiency and long lifespan. However, emerging fields such as micro- and nano-displays require further improvement of their electronic and optical properties. The key factor in understanding the limits of InGaN is the influence of strain on its basic physical properties. Unfortunately, many studies focus on investigating plastically relaxed InGaN layers, which are not used in devices. In this study, we investigate elastic strain in InGaN layers on nanoporous GaN. The InGaN layers are grown epitaxially on GaN substrates and formed into pillars from 8 μm to 100 nm in diameter using electron beam lithography and reactive ion etching. After porosifying the GaN layer underneath using electrochemical etching, InGaN layers relax elastically. Using scanning X-ray diffraction microscopy (SXDM), we mapped the strain distribution within the InGaN nanostructures and correlated it with the luminescence spectrum, the built-in electric field and the emission energy shift. The work presented in this paper benefits from the support received from the Polish Ministry of Science and Higher Education, dec. no. 2021/WK/11
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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".