Loss Analysis of P3 Laser Patterning of Perovskite Solar Cells via Hyperspectral Photoluminescence Imaging
Bibliographic record
Abstract
Upscaling perovskite solar cells and modules requires precise laser patterning for series interconnection and spatial characterization of cell parameters to understand laser–material interactions and their impact on performance. This study investigates the use of nanosecond (ns) and picosecond (ps) laser pulses at varying fluences for the P3 patterning step of perovskite solar cells. Hyperspectral photoluminescence (PL) imaging was employed to map key parameters such as optical bandgap energy, Urbach energy, and shunt resistance. The mappings were correlated with electrical measurements, revealing that both ns and ps lasers can be utilized for effective series interconnections with minimal performance losses at optimized fluences. Our findings provide a deeper understanding of fluence-dependent effects in P3 patterning. Moreover, the results demonstrate that the process window is robust, allowing for reasonable cell performance even with deviations from optimal parameters. This robustness, coupled with the scalability of the laser patterning process, emphasize its suitability for industrial module production.
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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".