Perovskite cell scaps simulation analysis

This article describes a final-year project that uses SCAPS-1D simulation data to create a structured analysis pipeline for Perovskite Solar Cells (PSCs). The project includes modules for studying dark I-V and illuminated J-V curves, layer thickness effects, temperature variation, and quantum efficiency, along with automated report generation and a machine-learning dashboard. The author credits GitHub Copilot for accelerating repetitive coding tasks, allowing more focus on the physics and analysis logic.

This is a submission for the GitHub Finish-Up-A-Thon Challenge I built a final-year project around Perovskite Solar Cell PSC simulation and analysis using SCAPS-1D data. The repository is not just a set of plots; it is a structured analysis pipeline that studies dark I-V behavior, illuminated J-V curves, layer thickness effects, temperature variation, quantum efficiency, and ETL/HTL sweeps. I also added an automated report-generation workflow and a machine-learning dashboard to make the simulation results easier to explore and interpret. GitHub repository: https://github.com/Asphane/perovskite-cell-scaps-simulation-analysis Demo video: https://drive.google.com/file/d/1zSmQiwvoN42cu0jrwqucPoSIW9Lh2u1g/view?usp=sharing This project started as raw simulation output and scattered analysis notebooks. I turned it into a cleaner, more complete project by organizing the work into separate modules for J-V analysis, dark I-V analysis, thickness optimization, QE, temperature sweep, and ETL/HTL sweep studies. I also added report generation so the findings could be documented more systematically instead of staying buried inside notebooks. GitHub Copilot helped me move faster on repetitive work: notebook boilerplate, plotting code, data handling, and report-generation scripts. It reduced the amount of time I spent writing mechanical code and let me focus more on the physics, the analysis logic, and the structure of the final project.