Passivation study of the amorphous–crystalline silicon interface formed using DC saddle‐field glow discharge
Bibliographic record
Abstract
Abstract The DC saddle‐field (DCSF) glow discharge method was used to deposit intrinsic a‐Si:H onto c‐Si to passivate the c‐Si surface. The effective minority carrier lifetime in the heterostructures as a function of the excess minority carrier density in the c‐Si wafers was measured. The results were then analyzed in the context of recombination associated with interface defect states using three known recombination models. The defect density and the charge density at the interface are inferred. In addition subsequent annealing of the samples was studied. It is shown that for our intrinsic a‐Si:H samples improvements in surface passivation are directly correlated with the reduction of interface defects and not the reduction of minority carrier concentration at the interface due to electric field. We have achieved excellent surface passivation with effective carrier lifetime >4 ms for an intrinsic a‐Si:H sample deposited at a process temperature of 200 °C and thickness of about 30 nm. It is also demonstrated that subsequent annealing, at 240 °C, of the samples which were prepared at process temperatures <240 °C greatly increases the effective lifetime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".