Modeling a monocrystalline Cu(In,Ga)Se<sub>2</sub>single junction solar cell grown on a GaAs substrate
Bibliographic record
Abstract
A polycrystalline Cu(In,Ga)Se2 (CIGS) single junction solar cell model is developed with dependencies on the molar fraction of In and Ga with a 0.8 eV Shockley-Read-Hall (SRH) trap level above the valence band. The simulated performance of this solar cell over molar fraction compares well to data published in the literature using the SRH minority carrier lifetime to fit the trend in open circuit voltage. The material parameters are then used as a foundation for a numerical model of a monocrystalline CIGS solar cell grown on a GaAs substrate with an emphasis on modeling the CIGS/GaAs interface where a molar fraction gradient in CIGS forms due to lattice mismatch induced inter-diffusion of Ga and In from the substrate and CIGS layers. Without strain effects due to the lattice mismatch, the CIGS monocrystalline solar cell has an efficiency of 18.6% under the AM1.5G spectrum (1000 W/m2) with a short circuit current density of 36.5 mA/cm2, an open circuit voltage of 0.66 V and a fill factor of 77.4%. However, when reasonable strain effects are considered, such as the formation of strained induced interface defects and threading dislocation densities (TDD), the efficiency degrades to 6% for TDD < 1x107 cm-2. The models are able to reproduce a similar structure’s measured performance using a TDD of 1.5×107 cm-2 and a surface recombination velocity of 108 cm/s at the CdS/CIGS interface.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".