通过调制过渡光伏的机器学习加速太阳能电池的缺陷分析
Yusheng Li1,2, Yiming Li1,3, Jiangjian Shi1
1Key Laboratory for Renewable Energy, Beijing Key Laboratory for New Energy Materials and Devices, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.
Fundamental research
|December 30, 2024
概括
机器学习分析调制过渡光伏 (m-TPV) 以快速,非破坏性的太阳能电池缺陷检测. 发现电荷捕获截面对于矿太阳能电池的重组性质比缺陷密度更为关键.
科学领域:
- 材料科学 材料科学 材料科学
- 半导体物理 半导体物理
- 人工智能的人工智能
背景情况:
- 对材料缺陷的非破坏性分析对于半导体设备至关重要.
- 了解太阳能电池缺陷机制对于提高性能至关重要.
研究的目的:
- 开发一种基于机器学习的方法,用于使用调制过渡光伏 (m-TPV) 分析太阳能电池缺陷.
- 为了将m-TPV测量与矿太阳能电池中的特定缺陷特性相关联.
主要方法:
- 澄清扰动光伏发电和衰变机制.
- 高通量电流瞬态模拟,创建一个大数据库的m-TPV曲线.
- 使用模拟数据库训练一个人工神经网络 (反向传播).
主要成果:
- 一个机器学习模型被成功训练,将m-TPV数据与矿太阳能电池缺陷特性相关联.
- 分析表明,电荷捕获截面显著影响电荷重组,而不是缺陷密度.
- 开发的方法使得多个参数缺陷分析成为可能.
结论:
- 拟议的机器学习方法为快速和非破坏性的太阳能电池缺陷分析提供了一个强大的工具.
- 这种方法可以更深入地了解不同缺陷参数对太阳能电池性能的影响.
- 这种方法预计将对未来的太阳能电池研究和开发有价值.
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