InGaAs/InP quantum well intermixing studied by cross-sectional scanning tunneling microscopy
Bibliographic record
Abstract
Cross-sectional scanning tunneling microscopy (STM) is used to study lattice matched InGaAs/InP quantum well (QW) intermixing induced by ion implantation and thermal annealing. Different strain development in QWs (determined by STM topography of elastic relaxation in cross sectionally cleaved samples) is found to be dependent upon the range of the implanted ions relative to the QWs. It is found that the quantum wells remain latticed matched to the barrier layers after intermixing when ions are implanted through the multiple quantum well (MQW) stack. A shallow implantation in which ions are implanted into the cap layer above the MQW stack leads to tensilely strained wells and compressively strained interfaces between wells and barriers. The strain development in the latter case is attributed to different degrees of interdiffusion on the group III and group V sublattices. Finite element elastic computations are used to extract the group V and group III interdiffusion length ratio, and results using different diffusion models are compared. A preferred group V interdiffusion in the case of shallow implantation is explained in terms of faster diffusing P related defects compared to In related defects. Images of as-grown QWs provide useful information about the growth technique related compositional fluctuations at the interfaces.
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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".