用光谱数据和神经网络，无损预测钙钛矿太阳能电池各层厚度。
Geometric Parameter Estimations of Perovskite Solar Cells Based on Optical Simulations
- 用卷积神经网络分析外量子效率，预测薄膜厚度。
- 贝叶斯优化使误差显著降低，透明钙钛矿表现更优。
- 适合光伏材料研发与质量控制人员快速评估器件结构。
本文提出一种非侵入式方法，通过卷积神经网络基于钙钛矿太阳能电池的外量子效率预测各层厚度。网络在光学性质恒定的厚度范围内训练，该范围限定了其应用边界。由于不透明钙钛矿存在光敏感性问题，透明钙钛矿上的网络表现更佳。为提升性能并降低均方根误差，研究尝试了不同采样方法、图像规格及贝叶斯超参数优化。采样方法仅带来微弱改进，而贝叶斯优化显著提升了精度。此外，还测试了输入规格与预处理方式的调整。结果表明，该方法在受控实验下具备可行性、高效性与有效性。
原文摘要 · Abstract (English)
This paper presents a non-invasive approach to estimate the layer thicknesses of perovskite solar cells. The thicknesses are predicted by a convolutional neural network that leverages the external quantum efficiency of a perovskite solar cell. The network is trained in thickness ranges where the optical properties are constant, and these ranges set the constraints for the network's application. Due to light sensitivity issues with opaque perovskites, the convolutional neural network showed better performance with transparent perovskites. To optimize the performance and reduce the root mean square error, we tried different sampling methods, image specifications, and Bayesian optimization for hyperparameter tuning. While sampling methods showed marginal improvement, implementing Bayesian optimization demonstrated high accuracy. Other minor optimization attempts include experimenting with input specifications and pre-processing approaches. The results confirm the feasibility, efficiency, and effectiveness of a convolution neural network for predicting perovskite solar cells' layer thicknesses based on controlled experiments.
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