Through-focus scanning optical microscopy (TSOM) is a high-efficient, low-costed, and nondestructive model-based optical nanoscale method with the capability of measuring semiconductor targets from nanometer to micrometer level. However, some instability issues resulted from lateral movement of the target and angular illuminating non-uniformity during the collection of through-focus (TF) images restrict TSOM’s potential applications so that considerable efforts are needed to align optical elements before the collection and correct the experimental TSOM image before differentiating the experimental TSOM image from simulated TSOM image. An improved corrected TSOM method using Fourier transform is herein presented in this paper. First, a series of experimental TF images are collected through scanning the objective of the optical microscopy, and the ideally simulated TF images are obtained by a full-vector formulation. Then, each experimental image is aligned to its corresponding simulated counterpart before constructing the TSOM image. Based on the analysis of precision and repeatability, this method demonstrates its capability to improve the performance of TSOM, and the promising possibilities in application of online and in-machine measurements.
Summary High‐throughput through‐focus scanning optical microscopy (TSOM) involves defocusing along the optical axis and capturing a series of defocus images and is useful in optical nanoscale measurement. However, TSOM is usually affected by its optical and mechanical noises. In this study, the issue of sensitivity and application in three‐dimensional (3D) multiple parameter measurement of TSOM is investigated. First, a TSOM system with objective scanning and its relative simulation algorithm are proposed. Second, based upon the system and algorithm, an experiment on an isolated Au line is performed and the corresponding matching library is established. Comparing the experimental TSOM image and simulated TSOM images of the library, 3D multiple parameter results of the Au line are extracted. Third, the precision of the system is analysed through a fidelity test particular for through‐focus images. According to this study, the system is robust to the optical and mechanical noises and hence could be useful in 3D multiple parametric measurement and high‐volume nanomanufacturing.
Unlike the optical information taken from a single in-focus image of general optical microscopy, through-focus scanning optical microscopy (TSOM) involves scanning a target through the focus and capturing of a series of images. These images can be used to conduct three-dimensional inspection and metrology with nanometer-scale lateral and vertical sensitivity. The sensitivity of TSOM strongly depends on many mechanical and optical factors. In this study, how illumination polarization and target structure affect the sensitivity of TSOM is analyzed. Firstly, the complete imaging procedure of the polarized light is investigated. Secondly, through-focus scanning results of different targets with two illumination polarizations are simulated using the finite-difference time-domain method. Thirdly, a few experiments are performed to verify the influence of illumination polarization and target structures on the sensitivity of TSOM. Both the results of the simulation and experiments illustrate an apparent influence of polarization on the sensitivity of inspecting the targets with center asymmetric structures. For enhanced sensitivity, illumination polarization should be perpendicular to the target texture. This conclusion is meaningful to adjust illumination polarization purposefully for different structure characteristics and improve the sensitivity of metrology.2 of 12 thus, the interaction between incident light wave and target surface is complicated, and the spatial distribution of scattered light wave depends on illumination polarization and target structures [6]. TSOM images with different OIRs correspond to different spatial distributions of scattered light [7]. Consequently, the illumination polarization and structure characteristics of the target strongly affect the sensitivity of TSOM [8,9]. To address the lacking systematic analysis of these effects, many works attempt to optimize illumination polarization according to different targets through pre-tests [10,11]. The drawbacks are long preparation time and low inspection efficiency. To understand the light scattering around the target surface and conduct the inspection in the most optimized condition, the effects on TSOM sensitivity brought by illumination polarization and target structure are analyzed and summarized in this paper.The reminder of this article is organized as follows: the complete image-forming procedure, from incidence of illumination to the imaging plane of the charge-coupled device (CCD), is analyzed in the second section. The simulation of the system is presented in the third section. A few experiments are performed in the fourth section to verify the simulation results. The simulation and experimental results are interpreted in the fifth section. A conclusion based on the analyses and results is proposed in the sixth section.
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 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.
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