Optimization of InxGa1−xN P-I-N Solar Cells: Achieving 21% Efficiency Through SCAPS-1D Modeling
Bibliographic record
Abstract
This study provides an in-depth numerical simulation to optimize the structure of InGaN-based p-i-n single homojunction solar cells using SCAPS-1D software. The cell comprised a p-type In0.6Ga0.4N layer, an intrinsic i-type In0.52Ga0.48N layer, and an n-type In0.48Ga0.52N layer. A systematic parametric optimization methodology was employed, involving a sequential investigation of doping concentrations, layer thicknesses, and indium composition to identify the optimal device configuration. Initial optimization of doping levels established optimal concentrations of Nd=1×1016 cm−3 for the p-layer and Na=8×1017 cm−3 for the n-layer. Subsequently, structural parameters were optimized through systematic variation of layer thicknesses while maintaining optimal doping concentrations. The comprehensive optimization culminated in the identification of an optimal device architecture featuring a p-type layer thickness of 0.2 μm, an intrinsic layer thickness of 0.4 μm, an n-type layer thickness of 0.06 μm, and an indium composition of x = 0.59 in the intrinsic layer. This fully optimized configuration achieved a maximum conversion efficiency (η) of 21.40%, a short-circuit current density (Jsc) of 28.2 mA/cm2, and an open-circuit voltage (Voc) of 0.874 V. The systematic optimization approach demonstrates the critical importance of simultaneous parameter optimization in achieving superior photovoltaic performance, with the final device configuration representing a 30.01% efficiency improvement compared to the baseline structure. These findings provide critical insights for improving the design and performance of InGaN-based solar cells, serving as a valuable reference for future experimental research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".