Bibliographic record
Abstract
Thermal equilibrium in a semiconductor refers to the state when the temperature is uniform and has been steady for a long time, and when there are no sources of energy other than heat, e.g., no applied electric field nor optical irradiation. Obviously, semiconductor devices will not be in thermal equilibrium when they are in operation. However, it turns out that parts of a device often remain in a state very close to thermal equilibrium and, furthermore, knowledge of the carrier concentrations in thermal equilibrium is often a good starting point for understanding how a device works. In this chapter, we briefly discuss the collision processes that tend to randomize the momenta of excited electrons and holes, then we introduce the thermal-equilibrium distribution function, develop some useful expressions for the carrier concentrations in equilibrium, and finish by considering the mean thermal velocity associated with an equilibrium distribution of electrons. The last property is a further step towards developing an understanding of current in diodes and transistors. Collisions In the previous chapter, we showed how the processes of recombination and generation alter the carrier concentrations in the conduction and valence bands. Under thermal-equilibrium conditions, the thermally activated band-to-band and chemical generation processes are operative, along with one or all of the following recombination mechanisms: radiative, RG centre, Auger.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.035 |
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".