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THz-TDS:通过在合成和实验数据上训练的神经网络提取二维材料的复杂导电性

Ben Beddoes, Nicholas Klokkou, Jon Gorecki

|April 12, 2025

概括

人工神经网络准确地从太赫兹光谱中提取石墨烯特性. 这种方法通过结合合成和实验的太赫兹时域光谱 (TDS) 光谱来提高新数据的准确性.

科学领域:

凝聚物质物理学凝聚物质物理学
材料科学是一种材料科学.
频谱学是一种光谱学.

背景情况:

太赫兹时域光谱 (TDS) 对于描述像石墨烯这样的二维材料至关重要.
太赫兹光学属性对电荷载体动力学 (移动性和散射时间) 很敏感.
从TDS数据中提取这些属性具有挑战性,并且可能产生错误.

研究的目的:

应用人工神经网络 (ANN) 来解释石墨烯的太赫兹光谱.
准确地从石墨烯单层中提取电荷载体的移动性和散射时间.
为了提高ANN的稳定性,用于分布外的太赫兹数据.

主要方法:

利用人工神经网络 (ANN) 来分析太赫兹频谱.
在一个数据集上训练了ANN,该数据集结合了合成生成和实验获得的太赫兹光谱.
评估了ANN在看不见的数据上的表现.

主要成果:

使用ANN成功提取电荷载体的移动性和散射时间.
与传统方法相比,在销售之外的数据上实现了更好的性能.
验证了综合合成和实验训练数据的有效性.

结论:

人工神经网络提供了一种强大的方法,用于对石墨烯的太赫兹光谱进行定量分析.
结合多样化的训练数据可以增强ANN对材料特征的概括能力.
这种方法提升了使用太赫兹光谱学进行二维材料属性的非破坏性探测.

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