Temperature and bias dependence of hydrogenated amorphous silicon – crystalline silicon heterojunction capacitance: the link to band bending and band offsets
Bibliographic record
Abstract
The temperature dependence of the capacitance–voltage data (C–V–T) of very high efficiency silicon heterojunction solar cells in a wide temperature range, up to 400 K, is analyzed. We show that the temperature dependence of the capacitance exhibits an anomalously large increase with temperature that cannot be explained under the usual depletion approximation. Using the complete analytical calculation of the capacitance, where the contribution of both types of carriers is taken into account, this large increase of capacitance with temperature of p-type hydrogenated amorphous silicon – n-type crystalline silicon ((p) a-Si:H – (n) c-Si) heterojunctions observed experimentally is reproduced. This increase of the capacitance is due to a strong inversion layer at the c-Si surface, which is promoted as the temperature increases. Further we show that the temperature dependence of the 1/C 2 versus applied reverse voltage (V a ) plot is as well strongly affected by the strong inversion layer at the c-Si surface. Consequently, the intercept of the linear extrapolation of 1/C 2 versus V a with the voltage axis (V int ) differs significantly from the total diffusion potential predicted by depletion capacitance theory. These underestimated values of the total diffusion potential can consequently lead to erroneous estimation of the band offsets. The temperature dependence of V int is considerably enhanced for the case of the full analytical calculation when compared with the depletion approximation approach. These data, obtained directly on the final solar cell device, thus confirm the existence of a surface strong inversion layer that was previously revealed by measurements performed by other techniques on dedicated or precursor devices, allowing one to get information on the band diagram and the heterointerface.
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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) | 0.001 | 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".