InGaAs ∕ InP quantum well intermixing studied by high-resolution x-ray diffraction and grazing incidence x-ray analysis
Bibliographic record
Abstract
Following a study of implantation enhanced interdiffusion of InGaAs∕InP multiple quantum well (MQW) structures by cross-sectional scanning tunneling microscopy (XSTM), the techniques of low temperature photoluminescence spectroscopy, high-resolution x-ray diffraction (HRXRD), and grazing incidence x-ray analysis (GIXA) are used to independently investigate the suitability of a square well model for the interdiffused MQW profiles, and the observed dependence of strain development as a function of the implanted ion range relative to the MQW stacks. In agreement with previous XSTM findings, when ions are implanted through the MQWs, HRXRD measurements indicate equivalent extents of interdiffusion occurring on both sublattices, while GIXA measurements further indicate the compositional profiles to be non-Fickian and compatible with uniformly broadened square well distributions. Following shallow ion implants (where ions are deposited between the MQWs and the sample surface), (004) HRXRD measurements indicate preferential group V interdiffusion. Dynamical simulations of the superlattice envelope in the (004) HRXRD rocking curves show the compositional profiles to be non-Fickian and compatible with a square well model for the broadened compositional profiles. Additional analysis of the (001) bilayer spacing from previously published XSTM linescan data for this structure is also consistent with this finding. Results of a preliminary photoluminescence and HRXRD investigation of disordering induced by indium implants and the effects of extended annealing on a series of MQW samples (with and without implantation) are presented. Implications for the implantation enhancement of interdiffusion in the InGaAs∕InP material system are discussed. The interpretation of quantum well interdiffusion experiments in this material system in terms of Fickian diffusion models warrants revision in light of the present findings.
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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".