Through-focus scanning optical microscopy (TSOM) is an economical, non-contact and nondestructive method for rapid measurement of three-dimensional nanostructures. There are two methods using TSOM image to measure the dimensions of one sample, including the library-matching method and the machine-learning regression method. The first has the defects of small measurement range and strict environmental requirements; the other has the disadvantages of feature extraction method greatly influenced by human subjectivity and low measurement accuracy. To solve the problems above, a TSOM dimensional measurement method based on deep-learning classification model is proposed. TSOM images are used to train the ResNet50 and DenseNet121 classification model respectively in this paper, and the test images are used to test the model, the classification result of which is taken as the measurement value. The test results showed that with the number of training linewidths increasing, the mean square error (MSE) of the test images is 21.05 nm² for DenseNet121 model and 31.84 nm² for ResNet50 model, both far lower than machine-learning regression method, and the measurement accuracy is significantly improved. The feasibility of using deep-learning classification model, instead of machine-learning regression model, for dimensional measurement is verified, providing a theoretical basis for further improvement on the accuracy of dimensional measurement.

中文翻译：

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一种基于深度学习分类模型的通焦扫描光学显微镜尺寸测量方法

全聚焦扫描光学显微镜 (TSOM) 是一种经济、非接触和无损的快速测量三维纳米结构的方法。使用TSOM图像测量一个样本的维度有两种方法，包括库匹配法和机器学习回归法。一是测量范围小，环境要求严格的缺陷；另一种是特征提取方法受人的主观性影响大，测量精度低的缺点。针对上述问题，提出了一种基于深度学习分类模型的TSOM维度测量方法。本文分别使用TSOM图像训练ResNet50和DenseNet121分类模型，使用测试图像对模型进行测试，其分类结果作为测量值。测试结果表明，随着训练线宽数量的增加，测试图像的均方误差（MSE）对于 DenseNet121 模型为 21.05 nm²，对于 ResNet50 模型为 31.84 nm²，均远低于机器学习回归方法，测量精度显着提高。验证了使用深度学习分类模型代替机器学习回归模型进行维度测量的可行性，为进一步提高维度测量的准确性提供了理论依据。两者都远低于机器学习回归方法，并且测量精度显着提高。验证了使用深度学习分类模型代替机器学习回归模型进行维度测量的可行性，为进一步提高维度测量的准确性提供了理论依据。两者都远低于机器学习回归方法，并且测量精度显着提高。验证了使用深度学习分类模型代替机器学习回归模型进行维度测量的可行性，为进一步提高维度测量的准确性提供了理论依据。