Lu Ang-Yu, Martins Luiz Gustavo Pimenta, Shen Pin-Chun, Chen Zhantao, Park Ji-Hoon, Xue Mantian, Han Jinchi, Mao Nannan, Chiu Ming-Hui, Palacios Tomás, Tung Vincent, Kong Jing
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Adv Mater. 2022 Aug;34(34):e2202911. doi: 10.1002/adma.202202911. Epub 2022 Jul 24.
2D transition metal dichalcogenides (TMDCs) with intense and tunable photoluminescence (PL) have opened up new opportunities for optoelectronic and photonic applications such as light-emitting diodes, photodetectors, and single-photon emitters. Among the standard characterization tools for 2D materials, Raman spectroscopy stands out as a fast and non-destructive technique capable of probing material's crystallinity and perturbations such as doping and strain. However, a comprehensive understanding of the correlation between photoluminescence and Raman spectra in monolayer MoS remains elusive due to its highly nonlinear nature. Here, the connections between PL signatures and Raman modes are systematically explored, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. This study's analysis further disentangles the strain and doping contributions from the Raman spectra through machine-learning models. First, a dense convolutional network (DenseNet) to predict PL maps by spatial Raman maps is deployed. Moreover, a gradient boosted trees model (XGBoost) with Shapley additive explanation (SHAP) to bridge the impact of individual Raman features in PL features is applied. Last, a support vector machine (SVM) to project PL features on Raman frequencies is adopted. This work may serve as a methodology for applying machine learning to characterizations of 2D materials.
具有强烈且可调节光致发光（PL）的二维过渡金属二硫属化物（TMDCs）为发光二极管、光电探测器和单光子发射器等光电子和光子应用开辟了新机遇。在二维材料的标准表征工具中，拉曼光谱作为一种快速且无损的技术脱颖而出，能够探测材料的结晶度以及诸如掺杂和应变等微扰。然而，由于其高度非线性的性质，对单层MoS2中光致发光与拉曼光谱之间的相关性仍缺乏全面理解。在此，系统地探索了PL特征与拉曼模式之间的联系，为关联PL和拉曼特征的物理机制提供了全面见解。本研究的分析还通过机器学习模型从拉曼光谱中进一步区分出应变和掺杂的贡献。首先，部署了一个密集卷积网络（DenseNet），通过空间拉曼图来预测PL图。此外，应用了具有Shapley加法解释（SHAP）的梯度提升树模型（XGBoost），以弥合各个拉曼特征对PL特征的影响。最后，采用了一个支持向量机（SVM），将PL特征投影到拉曼频率上。这项工作可作为将机器学习应用于二维材料表征的一种方法。
