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Through-focus scanning optical microscopy (TSOM) is an economical, noncontact 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 nmš for DenseNet121 model and 31.84 nmš 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.

Through-focus scanning optical microscopy (TSOM) is a model-based nanoscale metrology technique which combines conventional bright-field microscopy and the relevant numerical simulations. A TSOM image is generated after throughfocus scanning and data processing. However, the mechanical vibration and optical noise introduced into the TSOM image during image generation can affect the measurement accuracy. To reduce this effect, this paper proposes a imaging error compensation method for the TSOM image based on deep learning with U-Net.Here, the simulated TSOM image is regarded as the ground truth, and the U-Net is trained using the experimental TSOM images by means of a supervised learning strategy. The experimental TSOM image is first encoded and then decoded with the U-shaped structure of the U-Net. The difference between the experimental and simulated TSOM images is minimised by iteratively updating the weights and bias factors of the network, to obtain the compensated TSOM image. The proposed method is applied for optimising the TSOM images for nanoscale linewidth estimation. The results demonstrate that the proposed method performs as expected and provides a significant enhancement in accuracy.

Through-focus scanning optical microscopy (TSOM) is one of the recommended measurement methods in semiconductor manufacturing industry in recent years because of its rapid and nondestructive properties. As a computational imaging method, TSOM takes full advantage of the information from defocused images rather than only concentrating on focused images. In order to improve the accuracy of TSOM in nanoscale dimensional measurement, this paper proposes a two-input deep-learning TSOM method based on Convolutional Neural Network (CNN). The TSOM image and the focused image are taken as the two inputs of the network. The TSOM image is processed by three columns convolutional channels and the focused image is processed by a single convolution channel for feature extraction. Then, the features extracted from the two kinds of images are merged and mapped to the measuring parameters for output. Our method makes effective use of the image information collected by TSOM system, for which the measurement process is fast and convenient with high accuracy. The MSE of the method can reach 5.18 nm2 in the measurement of gold lines with a linewidth range of 247–1010 nm and the measuring accuracy is much higher than other deep-learning TSOM methods.

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