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.

中文翻译：

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遗传算法在太阳能电池中更有效的多层厚度优化中的应用

薄膜太阳能电池主要设计为类似于堆叠结构。优化这种堆叠结构中的层厚度对于获得太阳能电池的最佳效率至关重要。优化模拟中常用的方法，例如优化光学间隔层的厚度，是参数扫描。我们的模拟研究表明，在单层和多层优化中，与蛮力参数扫描方法相比，像遗传算法这样的元启发式方法的实现会产生明显更快和准确的搜索方法。虽然其他扫描方法也可以胜过蛮力方法，但它们在优化结果中并没有像我们的遗传算法那样始终表现出 $100\%$ 的准确度。我们使用了一个经过充分研究的基于 P3HT 的结构来测试我们的算法。