Threading dislocations in MBE grown AlInSb metamorphic buffers: Revealed and counted
Bibliographic record
Abstract
The authors compare four methods to investigate the threading dislocations (TDs) observed in metamorphic buffers used in the growth of InSb quantum well on GaAs (001) substrates. Three types of buffers with varying number of Al0.24In0.76Sb interlayers (N = 0, 1, and 3) were studied. Cross-sectional scanning transmission electron microscopy (STEM) revealed an effective dislocation filtering by the interlayers. Individual TDs were identified with atomic-force microscopy (AFM) as distinct morphological features of dislocation outcrops on the surface. Threading dislocation density (TDD) is reduced by 1 order of magnitude with three interlayers, consistent with the STEM observation. TDD measured with a scanning electron microscope in electron channeling contrast imaging (ECCI) mode agrees closely with the AFM analysis, except for the N = 0 buffer where the ECCI gives TDD lower by more than a factor of two. The etch pit density of N = 3 buffer, measured with a Nomarski differential interference contrast microscope after defect selective etching (DSE), is almost 1 order of magnitude lower than the TDD measured by AFM and ECCI. Due to the large pit size, the used etching recipe only works well for samples with TDD lower than 107 cm−2. AFM, ECCI, and DSE are excellent alternatives to transmission electron microscopy in the process of metamorphic buffer optimization. The AFM technique offers the additional advantage of high vertical resolution morphology mapping. Such capability is of great importance for the optimization of metamorphic buffers from the perspective of surface smoothness improvement.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".