Theory of the temperature dependent dielectric function of semiconductors: from bulk to surfaces. Application to GaAs and Si
Bibliographic record
Abstract
Abstract A novel, efficient method for calculating the temperature dependencies of the linear dielectric functions of semiconductor systems and its application are presented. The method follows an intuitive and natural path withab‐initiofinite temperature molecular dynamics providing the thermally perturbed atomic configurations, which are used as structural inputs for calculating the dielectric function. The effect of lattice dynamics, including quantum zero point vibration, on the electronic bands and dielectric function of crystalline (c‐) GaAs and Si as well as hydrogenated amorphous Si (a‐Si:H) is discussed. Our theoretical results for bulk c‐GaAs and c‐Si in the range from 0 to 1000 K are in good overall agreement with highly accurate ellipsometric measurements. The implementation of the method resolves a serious discrepancy in energy and line shape between experiment and the latest optical models, all of which neglect lattice dynamics, and provides information on the indirect gap and indirect optical transitions in c‐Si. For a‐Si:H, the calculated temperature dependent optical response combined with the vibrational spectroscopy provides detailed insight into electronic, dynamical properties, and stability of this important prototypical amorphous semiconductor material. At semiconductor surfaces, dynamical effects are expected to be even more pronounced due to reduced atom coordination and reconstruction. This is demonstrated for C(111) 2 × 1, an intensively studied but controversial surface of the quantum diamond crystal.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".