Carrier-Density-Wave Multiple Lifetime Imaging in a Multicrystalline Silicon Solar Cell Using Quantitative Heterodyne Lock-In Carrierography and Localized Current–Voltage Characteristics
Bibliographic record
Abstract
Trap-state kinetic parameters of a multicrystalline silicon solar cell were investigated using dynamic heterodyne lock-in carrierography (HeLIC) imaging under various conditions of illumination intensity and load resistance. Physical relaxation times associated with free-carrier-density-wave (CDW) band-to-band recombination, capture in and emission from two intraband-gap traps were obtained and imaged using camera-based HeLIC pixel frequency responses and a nonlinear rate equation model which provided physical insight on dynamic interactions between the CDW and the traps in the solar cell. An optoelectronic analog to the conventional diode equation was used to determine the solar cell parameters and, thus, enable the measurement oflocal I–Vcharacteristics imaged over the device surface. The dc photoluminescence image at an open circuit was used as a reference and its pixel statistics and electrical parameters (maximum power, series resistance, saturation current, and generation current) were imaged using the optoelectronic equivalent of the electrical Shockley equation. The optoelectronic lifetime images were then compared with lifetime images derived from these electrical solar cell parameters and the effects of the trap states were explored. Regions with maximum power were found to be associated with low trap concentrations exhibiting low thermal emission and capture rates, with the surface/shallow trap density affecting the maximum power. The trap type (surface versus bulk) limiting the solar cell maximum power was found to vary under different values of photovoltage from open circuit to short circuit.
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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".