Thin film composition determination by means of integrated intensity measurements
Bibliographic record
Abstract
A new method based on measurement of the absorption of an x-ray beam diffracted from a substrate has been used to determine the film composition in an InGaAs/InP nearly-lattice-matched single heterostructure. The absorption coefficient, μ InGaAs , of the InGaAs alloy was obtained by accurately measuring the InGaAs layer thickness and the integrated intensities of several diffractions of the InP substrate compared with the integrated intensities of the equivalent peaks of an InP reference crystal. By using the tabulated Ga, As and In absorption factors, the In content was then determined. It is shown that in the case of an InGaAs alloy the accuracy in In content determination can reach 1%. The absolute In content of the InGaAs epilayer was found to be 4%–5% larger than expected from the linear dependence of the lattice parameter on the alloy composition as stated by the Vegard law. This result has been confirmed by a diffraction profile auto-fitting software; with the assumption of the Vegard law the best fit of the 004 diffraction profile of the InGaAs/InP heterostructure could only be obtained with a 4.6% larger InGaAs thickness, to compensate for the larger In content as determined by the absorption measurement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".