Perovskite/crystalline-silicon (c-Si) tandem solar cells offer a viable roadmap for reaching power conversion efficiencies beyond 30%. In this configuration, however, the silicon cell now mainly receives an infrared rich illumination spectrum where the absorption coefficient of silicon is poor. To boost the light absorption in this wavelength interval, transmitted through the top cell, a solution is to introduce a dedicated nanoscale texture on the front side of the silicon bottom cell and tweaking the optical elements in its design. These optical elements are manifold, with tunable geometrical dimensions, layer thicknesses, and refractive indices. For optically optimizing nanostructured silicon solar cells, electromagnetic wave solving methods, such as the rigorous coupled-wave analysis (RCWA), are normally used but they become computationally infeasible when several design variables are involved in an optimization problem. In this work, a natural selection algorithm, known as the genetic algorithm, is coupled with RCWA for extracting the optimal values of various design parameters in four-terminal perovskite/c-Si tandem devices in parallel. Both two-side-contacted and interdigitated back-contacted silicon heterojunction cell structures, featuring an inverse nanopyramid grating texture on the front, are optimized for five interdependent variables using a genetic algorithm for the bottom cell application and benchmarked against their random pyramid textured analogs. Our study shows that an optimized inverse nanopyramid grating texture can outperform the standard random pyramid texture when considering incidence angle variations. More importantly, the study illustrates how a genetic algorithm can support modeling complex solar cell structures with numerous degrees of freedom.

中文翻译：

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遗传算法在四端钙钛矿/晶硅串联器件中的应用

钙钛矿/晶体硅 (c-Si) 串联太阳能电池为实现超过 30% 的功率转换效率提供了可行的路线图。然而，在这种配置中，硅电池现在主要接收富含红外线的照明光谱，其中硅的吸收系数很差。为了提高该波长区间内的光吸收，通过顶部电池传输，解决方案是在硅底部电池的正面引入专用的纳米级纹理，并在其设计中调整光学元件。这些光学元件是多种多样的，具有可调的几何尺寸、层厚和折射率。对于光学优化纳米结构硅太阳能电池，电磁波求解方法，如严格耦合波分析 (RCWA)，通常使用，但当优化问题中涉及多个设计变量时，它们在计算上变得不可行。在这项工作中，一种称为遗传算法的自然选择算法与 RCWA 相结合，用于并行提取四端钙钛矿/c-Si 串联器件中各种设计参数的最佳值。两侧接触和叉指背接触硅异质结电池结构均在正面具有反纳米金字塔光栅纹理，使用遗传算法针对底部电池应用程序针对五个相互依赖的变量进行了优化，并针对其随机金字塔纹理类似物进行了基准测试。我们的研究表明，当考虑入射角变化时，优化的逆纳米金字塔光栅纹理可以胜过标准的随机金字塔纹理。