Kfoury Patrick, Battie Yann, Naciri Aotmane En, Voue Michel, Chaoui Nouari
Opt Lett. 2024 Feb 1;49(3):574-577. doi: 10.1364/OL.514616.
Imaging ellipsometry is an optical characterization tool that is widely used to investigate the spatial variations of the opto-geometrical properties of thin films. As ellipsometry is an indirect method, an ellipsometric map analysis requires a modeling step. Classical methods such as the Levenberg-Marquardt algorithm (LM) are generally too time consuming to be applied on a large data set. In this way, an artificial neural network (ANN) approach was introduced for the analysis of an ellipsometric map. As a proof of concept this method was applied for the characterization of silver nanoparticles embedded in a poly-(vinyl alcohol) film. We demonstrate that the LM and ANN give similar results. However, the time required for the ellipsometric map analysis decreases from 15 days for the LM to 1 s for the ANN. This suggests that the ANN is a powerful tool for fast spectroscopic-ellipsometric-imaging analysis.
成像椭偏仪是一种光学表征工具，广泛用于研究薄膜光学几何特性的空间变化。由于椭偏测量是一种间接方法，椭偏测量图分析需要一个建模步骤。诸如Levenberg-Marquardt算法（LM）等经典方法通常耗时过长，无法应用于大数据集。因此，引入了一种人工神经网络（ANN）方法来分析椭偏测量图。作为概念验证，该方法被应用于表征嵌入聚（乙烯醇）薄膜中的银纳米颗粒。我们证明，LM和ANN给出了相似的结果。然而，椭偏测量图分析所需的时间从LM的15天减少到ANN的1秒。这表明ANN是快速光谱椭偏成像分析的有力工具。
