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首页> 外文期刊>Applied optics >Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network
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Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network

机译：演示了基于人工神经网络的完整椭圆仪表征方法的可行性

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摘要

Ellipsometry is an optical technique that is widely used for determining optical and geometrical properties of optical thin films. These properties are in general extracted from the ellipsometric measurement by solving an inverse problem. Classical methods like the Levenberg-Marquardt algorithm are generally too long, depending on direct calculation and are very sensitive to local minima. In this way, the neural network has proved to be an efficient tool for solving these kinds of problems in a very short time. Indeed, it is rapid and less sensitive to local minima than the classical inversion method. We suggest a complete neural ellipsometric characterization method for determining the index dispersion law and the thickness of a simple SiO_(2) or photoresist thin layer on Si, SiO_(2), and BK7 substrates. The influence of the training couples on the artificial neural network performance is also discussed.
机译：椭偏法是一种广泛用于确定光学薄膜的光学和几何性质的光学技术。这些性质通常是通过解决反问题而从椭偏测量中提取的。像Levenberg-Marquardt算法那样的经典方法通常太长，取决于直接计算，并且对局部最小值非常敏感。这样，神经网络已被证明是在很短的时间内解决这类问题的有效工具。实际上，它比经典的反演方法快速且对局部极小值不敏感。我们建议一种完整的神经椭圆表征方法，用于确定折射率色散定律以及Si，SiO_（2）和BK7衬底上的简单SiO_（2）或光致抗蚀剂薄层的厚度。还讨论了训练对对人工神经网络性能的影响。

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