Novel 3D reciprocal space visualization of strain relaxation in InSb on GaAs substrates
Bibliographic record
Abstract
This study introduces the reciprocal space polar visualization (RSPV) method, a novel approach for visualizing x-ray diffraction-based reciprocal space data. RSPV allows for the precise separation of tilt and strain, facilitating their individual analysis. InSb was grown by molecular beam epitaxy on two (001) GaAs substrates—one with no misorientation (sample A) and one with 2° surface misorientation from the (001) planes (sample B). There is a substantial lattice mismatch with the substrate, and this results in the generation of defects within the InSb layer during growth. To demonstrate RSPV’s effectiveness, a comprehensive comparison of surface morphology, dislocation density, strain, and tilt was conducted. RSPV revealed previously unobserved features of the 004 InSb Bragg peak, partially explained by the presence of threading dislocations and oriented abrupt steps. Surface morphologies examined by an atomic force microscope revealed that sample B had significantly lower root mean square roughness. Independent estimates of threading dislocation density (TDD) using x-ray diffraction (XRD) and electron channelling contrast imaging confirmed that sample B exhibited a significantly lower TDD than sample A. XRD methods further revealed unequal amounts of α- and β-type threading dislocations in both samples, contributing to an anisotropic Bragg peak. RSPV is shown to be a robust method for exploring 3D reciprocal space in any crystal, demonstrating that growing InSb on misoriented GaAs produced a higher-quality crystal compared to an on-orientation substrate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".