Through-focus Scanning Optical Microscopy Research Articles

Hole Edge Metrology and Inspection by Edge Diffractometry

Abstract This article introduces a novel hole edge inspection and metrology technology by edge diffractometry, which occurs when light interacts with the hole edge. The proposed method allows for simultaneous characterization of hole part error and edge roughness conditions. Edge diffraction occurs as light bends at a sharp edge. Such a diffractive fringe pattern, the so-called interferogram, is directly related to edge geometry and roughness. Image-based diffractometry inspection technology was developed to capture the diffractive fringe patterns. The collected fringe patterns were analyzed through statistical feature extraction methods, and numerical results such as roundness index, concentricity, and via edge roughness (VER) were obtained. The results indicated that hole 1 had an average VER of 0.665 μm and a roundness index of 0.95, while hole 2 was measured an average VER of 0.753 μm and a roundness index of 0.96. Through-focus scanning optical microscopy (TSOM) was also utilized to perform three-dimensional characterization of hole features along the depth direction. As a result, the proposed method could characterize hole part error and evaluate its roughness conditions. This study showed the potential to be adapted for automatic optical inspection for advancing microelectronics and semiconductor packaging technology.

Uncertainty optimization based on sensitivity analysis for TSOM method in multi-measurands metrology

Semiconductor manufacturing usually requires precise measurements on critical geometric parameters of those nano-scaled targets to perform the process control and promote the yields. Theoretically through-focus scanning optical microscopy (TSOM) method enables to extract measurement results with 3D nano-scaled resolution via searching for the solution leading to the minimal errors between simulated and experimented output images (so-called TSOM images) from the possible solution spaces of the measurands. However, the coupling effects among the measurands increase the uncertainties of the results, which hinders TSOM method in the multi-measurands metrology. Since the coupling effects can be decreased via optimizing measurement conditions, this paper proposes an optimization method based upon the sensitivity analysis. This method samples all the measurands to generate the coupling factors that quantitatively characterize the coupling effects and selects the optimized measurement condition which corresponds to the minimal coupling effects. The selected measurement condition is verified by a measurement on three Au lines, and the results prove that this condition leads to nano-scaled measuring errors and uncertainties. Contrary to local optimizations, the proposed method is valid for both linear and nonlinear measurement models, and can be generalized to other model-based metrologies.

Machine Learning Enhanced Optical Microscopy for the Rapid Morphology Characterization of Silver Nanoparticles.

The rapid characterization of nanoparticles for morphological information such as size and shape is essential for material synthesis as they are the determining factors for the optical, mechanical, and chemical properties and related applications. In this paper, we report a computational imaging platform to characterize nanoparticle size and morphology under conventional optical microscopy. We established a machine learning model based on a series of images acquired by through-focus scanning optical microscopy (TSOM) on a conventional optical microscope. This model predicts the size of silver nanocubes with an estimation error below 5% on individual particles. At the ensemble level, the estimation error is 1.6% for the averaged size and 0.4 nm for the standard deviation. The method can also identify the tip morphology of silver nanowires from the mix of sharp-tip and blunt-tip samples at an accuracy of 82%. Furthermore, we demonstrated online monitoring for the evolution of the size distribution of nanoparticles during synthesis. This method can be potentially extended to more complicated nanomaterials such as anisotropic and dielectric nanoparticles.

Subsurface Defect Detection Method of Optical Elements Based on Through-Focus Scanning Optical Microscopy

Subsurface Defect Detection Method of Optical Elements Based on Through-Focus Scanning Optical Microscopy

MEMS High Aspect Ratio Trench Three-Dimensional Measurement Using Through-Focus Scanning Optical Microscopy and Deep Learning Method

High-aspect-ratio structures have become increasingly important in MEMS devices. In situ, real-time critical dimension and depth measurement for high-aspect-ratio structures is critical for optimizing the deep etching process. Through-focus scanning optical microscopy (TSOM) is a high-throughput and inexpensive optical measurement method for critical dimension and depth measurement. Thus far, TSOM has only been used to measure targets with dimension of 1 μm or less, which is far from sufficient for MEMS. Deep learning is a powerful tool that improves the TSOM performance by taking advantage of additional intensity information. In this work, we propose a convolutional neural network model-based TSOM method for measuring individual high-aspect-ratio trenches on silicon with width up to 30 μm and depth up to 440 μm. Experimental demonstrations are conducted and the results show that the proposed method is suitable for measuring the width and depth of high-aspect-ratio trenches with a standard deviation and error of approximately a hundred nanometers or less. The proposed method can be applied to the semiconductor field.

Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring

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.

Through-focus scanning optical microscopy measurement based on machine learning

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

Defect height estimation via model-less TSOM under optical resolution

We propose a new method of through-focus scanning optical microscopy (TSOM) without a reference database, i.e., a model-less TSOM method. Building a TSOM reference database is time-consuming or even impractical in some TSOM applications that involve complex structures, such as 3D NAND, or irregular shapes such as defects. The proposed model-less TSOM method was used to determine just the height of defect particles, for the first time as far as we are aware. Defect height is the only relevant dimension for the display panel application. Specifically, we analyzed 40 organic light-emitting diode (OLED) surface defects using a lab-developed motion-free TSOM tool consisting of a 50× objective lens (numerical aperture (NA) 0.55), a 532-nm light source, an imaging detector with a 7.5-µm pitch, and a deformable mirror. The tool is in-line and capable of achieving high throughput non-destructively, both relevant features for industrial applications. We investigated linear regression relations between newly defined TSOM parameters (TSOM height, TSOM area and TSOM volume) and the defect heights, which were first measured by atomic force microscopy (AFM). Following defect classification based on in-focus images, we successfully found that the AFM height has a linear correlation with 50% TSOM height (H50%) within ± 20.3 nm (1σ) error over the range of 140 to 950 nm. The one-sigma error, i.e., 20.3 nm, was approximately λ/26 or 1/43 of the depth of focus (DOF) of the applied microscope.

A through‐focus scanning optical microscopy dimensional measurement method based on deep‐learning classification model

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.

Imaging error compensation method for through-focus scanning optical microscopy images based on deep learning.

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 through-focus 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.

Statistical character analysis for through-focus scanning optical microscopy in double floating variables measurement applications

Through-focus scanning optical microscopy (TSOM) is a model-based optical metrology for nano-scaled geometries with abilities of high efficiency, low cost, and non-destruction. The state-of-the-art studies on TSOM have been devoted to improving accuracies of single variable measurements or multiple variable measurements in limited degree of freedom, while the statistical significance of such applications has been generally ignored. Therefore, in this paper, a method requiring only one simulated model with multiple floating variables is proposed to analyze the statistical significance of TSOM. Given the advantage of ease of adjusting the simulated inputs, this method possesses abilities to qualitatively and quantitatively analyze variable correlations, repeatability accuracies, sensitivity of each variable as affected by the others, and the propagation of uncertainties in experimental parameters by first generating the simulated model for the TSOM system using finite difference time domain and Fourier optics; then, introducing subtle changes to the output - TSOM image. The proposed method is feasible for generalization to various nano-scaled structures and analyses of various experimental parameters, which helps improve the traceability of TSOM and its industrial applications.

Detecting nanoscale contamination in semiconductor fabrication using through-focus scanning optical microscopy.

This paper reports high-throughput, light-based, through-focus scanning optical microscopy (TSOM) for detecting industrially relevant sub-50 nm tall nanoscale contaminants. Measurement parameter optimization to maximize the TSOM signal using optical simulations made it possible to detect the nanoscale contaminants. Atomic force and scanning electron microscopies were used as reference methods for comparison.

Motion-free TSOM using a deformable mirror.

Through-focus scanning optical microscopy (TSOM) is a model-based optical metrology method that involves the scanning of a target through the focus of an optical microscope. Unlike a conventional optical microscope that directly extracts the diffraction-limited optical information from a single in-focus image, the TSOM method extracts nanometer scale sensitive information by matching the target TSOM data/image to reference TSOM data/images that are either experimentally or computationally collected. Therefore, the sensitivity and accuracy of the TSOM method strongly depends on the similarities between the conditions in which the target and reference TSOM images are taken or simulated, especially the lateral instability during through-focus scanning. As a remedy to the lateral instability, we proposed the application of adaptive optics to the through-focus scanning operation and initially developed a closed-loop system with a tip/tilt mirror and a Shack-Hartmann sensor, with which we were able to keep the plane position within peak-to-valley (PV) 33 nm. We then further developed a motion-free TSOM tool reducing the instability down to practically zero by the replacement of the tip/tilt mirror with a deformable mirror that performs through-focus scanning by deforming its mirror surface. The motion-free TSOM tool with a × 50 (NA 0.55) objective lens could provide a scanning range of up to ± 25 µm with a minimum step of 25 nm at a maximum update rate of 4 kHz. The tool was demonstrated to have a recognition accuracy of < 4 nm for critical dimension (CD) values in the range of 60 ∼ 120 nm with a reference TSOM image library generated by a Fourier modal method matching various observations conditions.

Lateral movement and angular illuminating non-uniformity corrected TSOM image using Fourier transform.

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.

Machine-learning models for analyzing TSOM images of nanostructures.

Through-focus scanning optical microscopy (TSOM) is an economical and nondestructive method for measuring three-dimensional nanostructures. After obtaining a TSOM image, a library-matching method is typically used to interpret optical intensity information and determine the dimensions of a measurement target. To further improve dimensional measurement accuracy, this paper proposes a machine learning method that extracts texture information from TSOM images. The method extracts feature vectors of TSOM images in terms of the Gray-level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Histogram of Oriented Gradient (HOG). We tested models trained with these vectors in isolation, in pairs, and a combination of all three to test seven possible feature vectors. Once normalized, these feature vectors were then used to train and test three machine-learning regression models: random forest, GBDT, and AdaBoost. Compared with the results of the library-matching method, the measurement accuracy of the machine learning method is considerably higher. When detecting dimensional features that fall into a wide range of sizes, the AdaBoost model used with the combined LBP and HOG feature vectors performs better than the others. For detecting dimensional features within a narrower range of sizes, the AdaBoost model combined with HOG feature extraction algorithm performs better.

Multiple parametric nanoscale measurements with high sensitivity based on through-focus scanning optical microscopy.

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.

Metrological sensitivity improvement of through-focus scanning optical microscopy by controlling illumination coherence.

We investigate the influence of the degree of illumination coherence on through-focus scanning optical microscopy (TSOM) in terms of metrological sensitivity. The investigation reveals that the local periodicity of the target object is a key structural parameter to consider when determining the optimal degree of illumination coherence for improved metrological sensitivity. The optimal coherence conditions for the TSOM inspection of several target objects are analyzed through numerical simulation.

Nondestructive shape process monitoring of three-dimensional, high-aspect-ratio targets using through-focus scanning optical microscopy

Low-cost, high-throughput and nondestructive metrology of truly three-dimensional (3D) targets for process control/monitoring is a critically needed enabling technology for high-volume manufacturing (HVM) of nano/micro technologies in multi-disciplinary areas. In particular, a survey of the typically used metrology tools indicates the lack of a tool that truly satisfies the HVM metrology needs of 3D targets, such as high-aspect-ratio (HAR) targets. Using HAR targets here we demonstrate that through-focus scanning optical microscopy (TSOM) is a strong contender to fill the gap for 3D shape metrology. Differential TSOM (D-TSOM) images are extremely sensitive to small and/or dissimilar types of 3D shape variations. Based on this, we here propose a TSOM method that involves creating a database of cross-sectional profiles of the HAR targets along with their respective D-TSOM signals. Using the database, we present a simple-to-use, low-cost, high-throughput and nondestructive process-monitoring method suitable for HVM of truly 3D targets, which also does not require optical simulations, making its use straightforward and automatable. Even though HAR targets are used for this demonstration, the similar process can be applied to any truly 3D targets with dimensions ranging from micro-scale to nano-scale. The TSOM method couples the advantage of analyzing truly isolated targets with the ability to simultaneously analyze many targets present in the large field-of-view of a conventional optical microscope.

Influence of Illumination Polarization and Target Structure on Measurement Sensitivity of Through-Focus Scanning Optical Microscopy

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.

Nondestructive shape process monitoring of three-dimensional high aspect ratio targets using through-focus scanning optical microscopy Optical Microscopy

Nondestructive shape process monitoring of three-dimensional high aspect ratio targets using through-focus scanning optical microscopy Optical Microscopy