Broadening effects and ergodicity in deep level photo-thermal spectroscopy of defect states in semi-insulating GaAs: A combined temperature-, pulse-rate- and time-domain study of defect state kinetics
Bibliographic record
Abstract
The technique of Deep Level Photo-Thermal Spectroscopy (DLPTS) is extended to the low temperature region in order to cover several defect states in semi-insulating GaAs. Measurements are taken at three different modes, temperature-scanned, pulse-rate-scanned, and time-scanned DLPTS. It is demonstrated that each mode provides unique information about the defect configuration, and the combination of the different modes offers a powerful tool for DLPTS studies of physical optoelectronic processes in SI-GaAs. The non-exponentiality/broadening of experimental data is extensively studied using the two prevalent broadening theories: the stretched exponential and the Gaussian distribution of activation energies. A hierarchical carrier emission model has been proposed for the stretched exponential behavior. Simulations indicate that the two broadening theories exhibit roughly similar broadening effects and good fits to the experimental data. The origin of this similarity indicates an ergodic equivalence of random energy distribution and the constrained hierarchical emission process.
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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) | 0.000 | 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) | 0.000 | 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 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".