Hyperspectral Photoluminescence Imaging for Spatially Resolved Determination of Electrical Parameters of Laser‐Patterned Perovskite Solar Cells
Bibliographic record
Abstract
Absolute calibrated hyperspectral photoluminescence (PL) imaging is utilized to access, in a simple and fast way, the spatial distribution of relevant solar cell parameters such as quasi‐Fermi level splitting, optical diode factor, Urbach energies E u, and shunt resistances R sh, without the need for electrical measurements. Since these metrics play a significant role in evaluating the process windows for electrical series interconnection by laser patterning, this approach is followed to systematically locate and quantify electrical losses that may occur as a result of the laser‐patterning process for monolithic series interconnection. It is shown that both picosecond and nanosecond laser pulses can be used for successful series interconnection. In both cases, only minor lateral material alterations occur, localized in a few μm wide region adjacent to the edges of the scribe lines. Furthermore, the acquisition and analysis of these hyperspectral PL datasets provide insights in the material removal process, from which it is concluded that the perovskite is rather resilient against the thermal impact of the laser.
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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 | 0.001 | 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) | 0.002 | 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".