Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells

TL;DR

This paper demonstrates that genetic algorithms significantly improve the efficiency and accuracy of optimizing layer thicknesses in multi-layer solar cells compared to traditional parameter sweep methods.

Contribution

The study introduces a genetic algorithm approach for optimizing solar cell layer thicknesses, outperforming brute-force methods in speed and accuracy.

Findings

Genetic algorithm reduces simulation count by 60.84% compared to brute-force.

GA achieves 100% accuracy in optimized results.

Faster and more accurate optimization in multi-layer solar cells.

Abstract

Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers' thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit $100\%$ accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use $60.84\%$ fewer simulations than the brute-force method.