All-laser scribing for thin-film CuInGaSe2 solar cells
Bibliographic record
Abstract
This paper describes the results from collaborative efforts in establishing an all-laser scribing technological capability for fabricating monolithically integrated CuInGaSe2 (CIGS) mini modules. The scribing parameters required for each of the three scribing steps (P1, P2, P3) were determined and optimized by using three specific samples fabricated at NREL. A fiber laser system using a single wavelength at 1064 nm was employed for the scribing, which allowed fine control of programmable pulse shape and pulse train. In general, the three scribe lines were clean and smooth. For the first time, two mini-modules, with an effective area of ~7-cm x 7-cm having 10 cell strips in series connection, were fabricated by the "all-laser-scribing" technology. Small-beam localized quantum efficiency (QE) analysis showed large non-uniformity across the cell arrays. Performance-degrading factors were investigated by photoluminescence (PL) and electroluminescence (EL) analysis. By increasing the Zn0:Al (AZO) thickness and rescribing with P3, the two reworked mini-modules showed substantial improvements in efficiency (best at 8.3%) with a decrease in series resistance and an increase in shunt resistance and fill factor.
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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".