Methods for determining deep defect concentration from dependence of excess carrier density and lifetime on illumination intensity
Bibliographic record
Abstract
Two methods are proposed for determining the deep defect concentration from dependences of excess majority carrier concentration and carrier lifetime on, respectively, illumination intensity and injection level. The methods are based on saturation of the excess majority carrier density with increasing illumination intensity and on an abrupt decrease in the lifetime of majority carriers with their increasing excess concentration, which takes place as a result of filling of the defect level by minority carriers. In contrast to the well known injection-level spectroscopy, both the methods make it possible to determine the density of a defect without knowing any of its parameters, such as energy level or recombination coefficient of electrons and holes. These methods are applied to boron-doped single-crystal silicon with radiation-induced deep defects of the phosphorus-vacancy, oxygen-vacancy and carbon-oxygen complex types. It is shown that with these methods it is possible to determine the density of only those deep defects which control free carrier density and lifetime and give rise to a significant difference between the excess concentrations and lifetimes of electrons and holes. The analysis is based on the assumption that (i) the density of deep defects is independent of the illumination intensity, (ii) processes of generation-recombination via deep defects are described within the Shockley-Read-Hall recombination theory and (iii) recombination via other defects, band-to-band recombination, Auger recombination, etc, are negligible.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".