The role of bulk and interface states on performance of a-Si : H p-i-n solar cells using reverse current–voltage technique
Bibliographic record
Abstract
The defect state densities in the bulk of the i-layer and at the p/i interface have been studied in hydrogenated amorphous silicon (a-Si?:?H) solar cells using reverse current?voltage (J?V) measurements. In this work the cells have been soaked with blue and red lights prior to measurements. The voltage-dependent reverse current has been analysed on the basis of thermal generation of the carriers from midgap states in the i-layer and the carrier injection through the p/i interface. Based on the reverse current behaviour, it has been analysed that at lower reverse bias (reverse voltage, Vr < 5?V) the defect states in the bulk of the i-layer and at higher bias (Vr ~ 25?V) the defect states at the p/i interface are contributing to the reverse currents. The applied reverse bias annealing (RBA) treatment on these cells shows more significant annihilation of defect states at the p/i interface as compared with the bulk of the i-layer. An analytical model is developed to explain the observed behaviour. There is good agreement between the theory and the experimental observations. The fitted defect state densities are 9.1 ? 1015?cm?3 and 8 ? 1018?cm?3 in the bulk of the i-layer and near the p/i interface, respectively. These values decrease to 2.5 ? 1015?cm?3 and 6 ? 1017?cm?3, respectively, in the samples annealed under reverse bias at 2?V.
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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 | 0.000 |
| 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".