Bibliographic record
Abstract
Precise quantitative assessment of c-Si wafer quality is of crucial importance for the development and manufacturing of high efficiency solar cells. For this purpose, lifetime samples are typically fabricated with very well cleaned and passivated surfaces. Under those conditions the measured effective lifetime τ eff is almost equal to the silicon bulk wafer lifetime τ wafer , i.e. a material related quality parameter. Those lifetime measurements are typically carried out with a photo-conductance decay method (PCM) e.g. with a Sinton-WCT tool. The measurement result is an effective excess carrier lifetime τ eff which typically exhibits a strong dependence on the excess carrier injection density Δn within the wafer. Stating τ eff –values thus necessitates to specifiy Δn . The PV community typically reports at a fixed Δn in the range of 1×10 14 cm -3 to 1×10 16 cm -3 or for varying wafer doping density N dop at Δn = N dop /10. The latter allows for a comparison from the point of view of the Shockley-Read-Hall (SRH) formalism. Unfortunately, the impact of a certain lifetime for device performance changes with N dop , due to the law of mass action. In this paper a wafer doping density dependent Δn which is relevant for the injection density at maximum power point (MPP) is derived. This Δn @MPP shows a contrary behaviour compared to the often used and accepted reporting method to set Δn = N dop /10. Additionally, a wafer doping density independent material quality parameter, called material saturation current density j 0,mat at MPP, is proposed to improve the comparability of measured effective lifetimes of differently doped wafers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".