The Experts below are selected from a list of 24171 Experts worldwide ranked by ideXlab platform
Peng Bu - One of the best experts on this subject based on the ideXlab platform.
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in situ aberration measurement technique based on principal component analysis of Aerial Image
Optics Express, 2011Co-Authors: Lifeng Duan, Anatoly Y. Bourov, Xiangzhao Wang, Bo Peng, Peng BuAbstract:We propose a novel in situ aberration measurement technique for lithographic projection lens by use of Aerial Image based on principal component analysis (AMAI-PCA). The Aerial Image space, principal component space and Zernike space are introduced to create a transformation model between Aerial Images and Zernike coefficients. First the aberration-induced Aerial Images of measurement marks are simulated to form an Aerial Image space with a statistical Box–Behnken design pattern. The Aerial Image space is then represented by their principal components based on principal component analysis. The principal component coefficients of the Aerial Images are finally connected with Zernike coefficients by a regression matrix through regression analysis. Therefore in situ aberration measurement can be achieved based on the regression matrix and the principal component coefficients of the detected Aerial Images. The measurement performance of the proposed AMAI-PCA technique is demonstrated superior compared to that of the conventional TAMIS technique by using a lithographic simulator tool (Prolith). We also tested the actual performance of AMAI-PCA technique on a prototype wafer exposure tool. The testing results show our proposed technique can rapidly measure the aberrations up to high-order Zernike polynomial term with 1σ repeatability of 0.5nm to 2.3nm depending on the aberration type and range.
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In situ aberration measurement technique based on principal component analysis of Aerial Image
Optics Express, 2011Co-Authors: Lifeng Duan, Anatoly Y. Bourov, Xiangzhao Wang, Bo Peng, Peng BuAbstract:We propose a novel in situ aberration measurement technique for lithographic projection lens by use of Aerial Image based on principal component analysis (AMAI-PCA). The Aerial Image space, principal component space and Zernike space are introduced to create a transformation model between Aerial Images and Zernike coefficients. First the aberration-induced Aerial Images of measurement marks are simulated to form an Aerial Image space with a statistical Box-Behnken design pattern. The Aerial Image space is then represented by their principal components based on principal component analysis. The principal component coefficients of the Aerial Images are finally connected with Zernike coefficients by a regression matrix through regression analysis. Therefore in situ aberration measurement can be achieved based on the regression matrix and the principal component coefficients of the detected Aerial Images. The measurement performance of the proposed AMAI-PCA technique is demonstrated superior compared to that of the conventional TAMIS technique by using a lithographic simulator tool (Prolith). We also tested the actual performance of AMAI-PCA technique on a prototype wafer exposure tool. The testing results show our proposed technique can rapidly measure the aberrations up to high-order Zernike polynomial term with 1σ repeatability of 0.5nm to 2.3nm depending on the aberration type and range. © 2011 Optical Society of America.
Takashi Naito - One of the best experts on this subject based on the ideXlab platform.
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Road Image update using in-vehicle camera Images and Aerial Image
IEEE Intelligent Vehicles Symposium, Proceedings, 2011Co-Authors: Masafumi Noda, Ichiro Ide, Tomokazu Takahashi, Daisuke Deguchi, Hiroshi Murase, Yoshiko Kojima, Takashi NaitoAbstract:Road Image is becoming important for several applications such as car navigation systems, traffic environment research, city modeling. Usually, a road Image can be obtained from an Aerial Image but the resolution of the Aerial Image is often low, or it contains occlusions by obstacles. Therefore, the update of road Image is required. In this paper, we propose a road Image mosaicing method using in-vehicle camera Images and an Aerial Image. We first perform Image registration of road regions between these Images, and then, we generate a large road Image by performing Image mosaicing of road regions in invehicle camera Images. In an experiment, we achieved resolution improvement and occlusions removal, and also succeeded in update of a large road Image.
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vehicle ego localization by matching in vehicle camera Images to an Aerial Image
International Conference on Computer Vision, 2010Co-Authors: Masafumi Noda, Tomokazu Takahashi, Daisuke Deguchi, Hiroshi Murase, Yoshiko Kojima, Takashi NaitoAbstract:Obtaining an accurate vehicle position is important for intelligent vehicles in supporting driver safety and comfort. This paper proposes an accurate ego-localization method by matching in-vehicle camera Images to an Aerial Image. There are two major problems in performing an accurate matching: (1) Image difference between the Aerial Image and the in-vehicle camera Image due to view-point and illumination conditions, and (2) occlusions in the in-vehicle camera Image. To solve the first problem, we use the SURF Image descriptor, which achieves robust feature-point matching for the various Image differences. Additionally, we extract appropriate feature-points from each road-marking region on the road plane in both Images. For the second problem, we utilize sequential multiple in-vehicle camera frames in the matching. The experimental results demonstrate that the proposed method improves both ego-localization accuracy and stability.
Lifeng Duan - One of the best experts on this subject based on the ideXlab platform.
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in situ aberration measurement technique based on principal component analysis of Aerial Image
Optics Express, 2011Co-Authors: Lifeng Duan, Anatoly Y. Bourov, Xiangzhao Wang, Bo Peng, Peng BuAbstract:We propose a novel in situ aberration measurement technique for lithographic projection lens by use of Aerial Image based on principal component analysis (AMAI-PCA). The Aerial Image space, principal component space and Zernike space are introduced to create a transformation model between Aerial Images and Zernike coefficients. First the aberration-induced Aerial Images of measurement marks are simulated to form an Aerial Image space with a statistical Box–Behnken design pattern. The Aerial Image space is then represented by their principal components based on principal component analysis. The principal component coefficients of the Aerial Images are finally connected with Zernike coefficients by a regression matrix through regression analysis. Therefore in situ aberration measurement can be achieved based on the regression matrix and the principal component coefficients of the detected Aerial Images. The measurement performance of the proposed AMAI-PCA technique is demonstrated superior compared to that of the conventional TAMIS technique by using a lithographic simulator tool (Prolith). We also tested the actual performance of AMAI-PCA technique on a prototype wafer exposure tool. The testing results show our proposed technique can rapidly measure the aberrations up to high-order Zernike polynomial term with 1σ repeatability of 0.5nm to 2.3nm depending on the aberration type and range.
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In situ aberration measurement technique based on principal component analysis of Aerial Image
Optics Express, 2011Co-Authors: Lifeng Duan, Anatoly Y. Bourov, Xiangzhao Wang, Bo Peng, Peng BuAbstract:We propose a novel in situ aberration measurement technique for lithographic projection lens by use of Aerial Image based on principal component analysis (AMAI-PCA). The Aerial Image space, principal component space and Zernike space are introduced to create a transformation model between Aerial Images and Zernike coefficients. First the aberration-induced Aerial Images of measurement marks are simulated to form an Aerial Image space with a statistical Box-Behnken design pattern. The Aerial Image space is then represented by their principal components based on principal component analysis. The principal component coefficients of the Aerial Images are finally connected with Zernike coefficients by a regression matrix through regression analysis. Therefore in situ aberration measurement can be achieved based on the regression matrix and the principal component coefficients of the detected Aerial Images. The measurement performance of the proposed AMAI-PCA technique is demonstrated superior compared to that of the conventional TAMIS technique by using a lithographic simulator tool (Prolith). We also tested the actual performance of AMAI-PCA technique on a prototype wafer exposure tool. The testing results show our proposed technique can rapidly measure the aberrations up to high-order Zernike polynomial term with 1σ repeatability of 0.5nm to 2.3nm depending on the aberration type and range. © 2011 Optical Society of America.
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Aerial Image model and application to aberration measurement
Optical Microlithography XXIII, 2010Co-Authors: Anatoly Y. Bourov, Zhiyong Yang, Liang Li, Fan Wang, Lifeng DuanAbstract:In this paper, we present a streamlined Aerial Image model that is linear with respect to projection optic's aberrations. The model includes the impact of the NA, partial coherence, as well as the aberrations on the full Aerial Image as measured on an x-z grid. The model allows for automatic identification of Image's primary degrees of freedom, such as bananicity and Y-icity among others. The model is based on physical simulation and statistical analysis. Through several stages of multivariate analysis a reduced dimensionality description of Image formation is obtained, using principal components on the Image side and lumped factors on the parameter side. The modeling process is applied to the Aerial Images produced by the alignment sensor in a 0.75NA ArF scanner while the tool is integration mode and aberration levels are high. Approximately 20 principal components are found to have a high signal-to-noise ratio in the Image set produced by varying illumination conditions and considering aberrations represented by 33 Zernike polynomials. The combined coefficients are extracted and the measurement repeatability is presented. The analysis portion of the model is then applied to the measured coefficients and a subset of projection lens' aberrations are solved for. © 2010 SPIE.
Masafumi Noda - One of the best experts on this subject based on the ideXlab platform.
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Road Image update using in-vehicle camera Images and Aerial Image
IEEE Intelligent Vehicles Symposium, Proceedings, 2011Co-Authors: Masafumi Noda, Ichiro Ide, Tomokazu Takahashi, Daisuke Deguchi, Hiroshi Murase, Yoshiko Kojima, Takashi NaitoAbstract:Road Image is becoming important for several applications such as car navigation systems, traffic environment research, city modeling. Usually, a road Image can be obtained from an Aerial Image but the resolution of the Aerial Image is often low, or it contains occlusions by obstacles. Therefore, the update of road Image is required. In this paper, we propose a road Image mosaicing method using in-vehicle camera Images and an Aerial Image. We first perform Image registration of road regions between these Images, and then, we generate a large road Image by performing Image mosaicing of road regions in invehicle camera Images. In an experiment, we achieved resolution improvement and occlusions removal, and also succeeded in update of a large road Image.
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vehicle ego localization by matching in vehicle camera Images to an Aerial Image
International Conference on Computer Vision, 2010Co-Authors: Masafumi Noda, Tomokazu Takahashi, Daisuke Deguchi, Hiroshi Murase, Yoshiko Kojima, Takashi NaitoAbstract:Obtaining an accurate vehicle position is important for intelligent vehicles in supporting driver safety and comfort. This paper proposes an accurate ego-localization method by matching in-vehicle camera Images to an Aerial Image. There are two major problems in performing an accurate matching: (1) Image difference between the Aerial Image and the in-vehicle camera Image due to view-point and illumination conditions, and (2) occlusions in the in-vehicle camera Image. To solve the first problem, we use the SURF Image descriptor, which achieves robust feature-point matching for the various Image differences. Additionally, we extract appropriate feature-points from each road-marking region on the road plane in both Images. For the second problem, we utilize sequential multiple in-vehicle camera frames in the matching. The experimental results demonstrate that the proposed method improves both ego-localization accuracy and stability.
Anatoly Y. Bourov - One of the best experts on this subject based on the ideXlab platform.
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in situ aberration measurement technique based on principal component analysis of Aerial Image
Optics Express, 2011Co-Authors: Lifeng Duan, Anatoly Y. Bourov, Xiangzhao Wang, Bo Peng, Peng BuAbstract:We propose a novel in situ aberration measurement technique for lithographic projection lens by use of Aerial Image based on principal component analysis (AMAI-PCA). The Aerial Image space, principal component space and Zernike space are introduced to create a transformation model between Aerial Images and Zernike coefficients. First the aberration-induced Aerial Images of measurement marks are simulated to form an Aerial Image space with a statistical Box–Behnken design pattern. The Aerial Image space is then represented by their principal components based on principal component analysis. The principal component coefficients of the Aerial Images are finally connected with Zernike coefficients by a regression matrix through regression analysis. Therefore in situ aberration measurement can be achieved based on the regression matrix and the principal component coefficients of the detected Aerial Images. The measurement performance of the proposed AMAI-PCA technique is demonstrated superior compared to that of the conventional TAMIS technique by using a lithographic simulator tool (Prolith). We also tested the actual performance of AMAI-PCA technique on a prototype wafer exposure tool. The testing results show our proposed technique can rapidly measure the aberrations up to high-order Zernike polynomial term with 1σ repeatability of 0.5nm to 2.3nm depending on the aberration type and range.
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In situ aberration measurement technique based on principal component analysis of Aerial Image
Optics Express, 2011Co-Authors: Lifeng Duan, Anatoly Y. Bourov, Xiangzhao Wang, Bo Peng, Peng BuAbstract:We propose a novel in situ aberration measurement technique for lithographic projection lens by use of Aerial Image based on principal component analysis (AMAI-PCA). The Aerial Image space, principal component space and Zernike space are introduced to create a transformation model between Aerial Images and Zernike coefficients. First the aberration-induced Aerial Images of measurement marks are simulated to form an Aerial Image space with a statistical Box-Behnken design pattern. The Aerial Image space is then represented by their principal components based on principal component analysis. The principal component coefficients of the Aerial Images are finally connected with Zernike coefficients by a regression matrix through regression analysis. Therefore in situ aberration measurement can be achieved based on the regression matrix and the principal component coefficients of the detected Aerial Images. The measurement performance of the proposed AMAI-PCA technique is demonstrated superior compared to that of the conventional TAMIS technique by using a lithographic simulator tool (Prolith). We also tested the actual performance of AMAI-PCA technique on a prototype wafer exposure tool. The testing results show our proposed technique can rapidly measure the aberrations up to high-order Zernike polynomial term with 1σ repeatability of 0.5nm to 2.3nm depending on the aberration type and range. © 2011 Optical Society of America.
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Aerial Image model and application to aberration measurement
Optical Microlithography XXIII, 2010Co-Authors: Anatoly Y. Bourov, Zhiyong Yang, Liang Li, Fan Wang, Lifeng DuanAbstract:In this paper, we present a streamlined Aerial Image model that is linear with respect to projection optic's aberrations. The model includes the impact of the NA, partial coherence, as well as the aberrations on the full Aerial Image as measured on an x-z grid. The model allows for automatic identification of Image's primary degrees of freedom, such as bananicity and Y-icity among others. The model is based on physical simulation and statistical analysis. Through several stages of multivariate analysis a reduced dimensionality description of Image formation is obtained, using principal components on the Image side and lumped factors on the parameter side. The modeling process is applied to the Aerial Images produced by the alignment sensor in a 0.75NA ArF scanner while the tool is integration mode and aberration levels are high. Approximately 20 principal components are found to have a high signal-to-noise ratio in the Image set produced by varying illumination conditions and considering aberrations represented by 33 Zernike polynomials. The combined coefficients are extracted and the measurement repeatability is presented. The analysis portion of the model is then applied to the measured coefficients and a subset of projection lens' aberrations are solved for. © 2010 SPIE.
