Using hybrid deep learning to predict spectral responses of quantum dot-embedded nanoporous thin-film solar cells

In this study, we propose an innovative design for nanoporous Si thin film (NPTF) solar cell, seamlessly integrated with semiconducting (CdSe)ZnS Quantum Dots (QDs), without the need for additional metal-dielectric interfaces to attain plasmonic like effects. The intricate network of randomized nano-scaled pores within thin film creates similar enhancement, complemented by QDs inducing excitonic resonances, and amplifying localized electromagnetic field density. To evaluate the spectral responses of the structure we use a supervised trained surrogate model. To train the model, we generate ground truth datasets by solving Maxwell's equations in the design domain and, subsequently, applying charge carrier dynamics model to evaluate the external quantum efficiency (EQE). To predict the spectral response for this stochastic design with randomized pore and QD positions, we feed the ground truth data to a customized Hybrid Deep Learning (HDL) model through in-vitro geometric features fused with dynamic features of QDs. The dynamic features are extracted using an electron dynamics (ED) study. We then evaluate the prediction accuracy of our HDL model. Results show that our designed model can predict absorptivity with an accuracy of R 2 >  0.96, and EQE with an accuracy of R 2 >  0.98. This investigation highlights the potential of coupling nanoporous thin film solar cells with QDs, an observed localized enhancement phenomenon, and HDL model to achieve high-performance thin-film solar cells, characterized by improved external quantum efficiency without using metal-dielectric interfaces.

在这项研究中，我们提出了一种纳米多孔硅薄膜（NPTF）太阳能电池的创新设计，它与半导体（CdSe）ZnS量子点（QDs）无缝集成，无需额外的金属 - 电介质界面即可获得类等离子体效应。薄膜内随机纳米级孔隙的复杂网络产生了类似的增强效果，再加上量子点诱导的激子共振以及局部电磁场密度的放大。为了评估该结构的光谱响应，我们使用了一个有监督训练的代理模型。为了训练该模型，我们通过在设计域中求解麦克斯韦方程组生成基础事实数据集，随后应用电荷载流子动力学模型来评估外量子效率（EQE）。为了预测具有随机孔隙和量子点位置的这种随机设计的光谱响应，我们将基础事实数据通过融合量子点动态特征的体外几何特征输入到一个定制的混合深度学习（HDL）模型中。动态特征是通过电子动力学（ED）研究提取的。然后我们评估了我们的HDL模型的预测准确性。结果表明，我们设计的模型能够以R²>0.96的精度预测吸收率，以R²>0.98的精度预测外量子效率。这项研究强调了将纳米多孔薄膜太阳能电池与量子点耦合的潜力、观察到的局部增强现象以及HDL模型在实现高性能薄膜太阳能电池方面的作用，其特点是在不使用金属 - 电介质界面的情况下提高了外量子效率。