Mr. Yasutaka Toyoda Profile

Yasutaka Toyoda

at Hitachi High-Tech Corp

SPIE Involvement:

Author

Publications (17)

Proceedings Article | 10 April 2024 Presentation + Paper

A flexible deep learning based approach for SEM image denoising

Jun Chen, Xinheng Jang, Keisuke Goto, Takashi Tsutsumi, Yasutaka Toyoda

Proceedings Volume 12955, 129550B (2024) https://doi.org/10.1117/12.3011135

KEYWORDS: Scanning electron microscopy, Shrinkage, Denoising, Metrology, Image denoising, Deep learning, Inspection, Image enhancement

Read Abstract +

Proceedings Article | 27 April 2023 Poster + Paper

Comparison between supervised and self-supervised deep learning for SEM image denoising

Tomoyuki Okuda, Jun Chen, Takahiro Motoyoshi, Ryou Yumiba, Masayoshi Ishikawa, Yasutaka Toyoda

Proceedings Volume 12496, 124963F (2023) https://doi.org/10.1117/12.2660673

KEYWORDS: Scanning electron microscopy, Image quality, Image denoising, Signal to noise ratio, Image processing, Denoising, Inspection, Deep learning, Evolutionary algorithms, Artificial neural networks

Read Abstract +

Proceedings Article | 22 February 2021 Presentation + Paper

Massive metrology and inspection solution for EUV by area inspection SEM with machine learning technology

Tsuyoshi Kondo, Naoma Ban, Yasushi Ebizuka, Yasutaka Toyoda, Yukari Yamada, Taeko Kashiwa, Hirohito Koike, Hiroyuki Shindo, Anne-Laure Charley, Mohamed Saib, Frieda Van Roey, Peter De Bisschop, Danilo De Simone, Christophe Beral, Gian Lorusso

Proceedings Volume 11611, 1161111 (2021) https://doi.org/10.1117/12.2583691

KEYWORDS: Inspection, Scanning electron microscopy, Metrology, Extreme ultraviolet, Machine learning, Logic, Stochastic processes, Process control, Critical dimension metrology, Time metrology

Read Abstract +

Proceedings Article | 20 March 2020 Paper

A trainable die-to-database for fast e-Beam inspection: learning normal images to detect defects

Masanori Ouchi, Masayoshi Ishikawa, Shinichi Shinoda, Yasutaka Toyoda, Ryo Yumiba, Hiroyuki Shindo, Masayuki Izawa

Proceedings Volume 11325, 113252F (2020) https://doi.org/10.1117/12.2551456

KEYWORDS: Inspection, Scanning electron microscopy, Defect detection, Image resolution, Defect inspection, Semiconducting wafers, Manufacturing, Extreme ultraviolet lithography, Semiconductors, Wafer inspection

Read Abstract +

Proceedings Article | 26 March 2019 Paper

Deep learning's impact on contour extraction for design based metrology and design based inspection

Ryo Yumiba, Masayoshi Ishikawa, Shinichi Shinoda, Shigetoshi Sakimura, Yasutaka Toyoda, Hiroyuki Shindo, Masayuki Izawa

Proceedings Volume 10959, 1095912 (2019) https://doi.org/10.1117/12.2514898

KEYWORDS: Image segmentation, Scanning electron microscopy, Image processing, Data modeling, Metrology, Surgery, Feature extraction, Shape analysis, Statistical modeling, Pattern recognition

Read Abstract +

Proceedings Article | 26 March 2019 Paper

Deep learning's impact on pattern matching for design based metrology and design based inspection

Shuyang Dou, Shinichi Shinoda, Masayoshi Ishikawa, Ryo Yumiba, Yasutaka Toyoda, Hiroyuki Shindo, Masayuki Izawa, Masanori Ouchi

Proceedings Volume 10959, 109592G (2019) https://doi.org/10.1117/12.2514921

KEYWORDS: Scanning electron microscopy, Image processing, Metrology, Inspection, Image analysis, Lithography, 3D modeling, Image quality, Computer simulations, Device simulation

Read Abstract +

Proceedings Article | 25 March 2016 Paper

Focus measurement using SEM image analysis of circuit pattern

Shinichi Shinoda, Yasutaka Toyoda, Yutaka Hojo, Hitoshi Sugahara, Hiroyuki Sindo

Proceedings Volume 9778, 97783K (2016) https://doi.org/10.1117/12.2229089

KEYWORDS: Scanners, Scanning electron microscopy, Image analysis, Semiconducting wafers, Shape analysis, Semiconductors, Optical fabrication, Metrology, Process control, Critical dimension metrology

Read Abstract +

Proceedings Article | 2 April 2014 Paper

Contour-based metrology for complex 2D shaped patterns printed by multiple-patterning process

Daisuke Fuchimoto, Toru Ishimoto, Hiroyuki Shindo, Hitoshi Sugahara, Yasutaka Toyoda, Julien Mailfert, Peter De Bisschop

Proceedings Volume 9050, 90500V (2014) https://doi.org/10.1117/12.2046783

KEYWORDS: Personal protective equipment, Optical proximity correction, Optical lithography, Metrology, Etching, Semiconducting wafers, Metals, Lithography, Photomasks, Reliability

Read Abstract +

We developed a new measurement method enabling to quantitatively and accurately evaluate 2D pattern shapes, which becomes critical in patterning control of Metal layer patterns transferred by Litho-Etch-Litho-Etch (LELE) process. In LELE, a split patterning of a Metal-A (MA) layer and a Metal-B (MB) layer makes patterning control more challenging. Hence, it is essential to evaluate the shape of transferred patterns after final etching in order to verify that the patterning control of MA and MB layer patterns is performed within an allowable budget. For this, our Pattern Shape Quantification (PSQ) method [1][2][3], which enables to measure dimensional difference of the transferred pattern shape from their target-design, is an effective metrology. Patterns transferred through a LELE process contain the effects of two types of shape modifications. The first is the fidelity of the individual pattern shapes (e.g. pattern-end pull-back or push-out) whose determinative factors are adopted design (e.g. OPC and SRAF), process condition (of e.g. lithography and etching), etc. The second is the shift in position between MA and MB patterns induced by Pattern Placement Error (PPE) of MB with respect to MA. That means that the edge-placement errors (EPE) in the final pattern are not only due to the fidelity of the transferred pattern shape, but are also impacted by the PPE. Also, a space between MA and MB patterns will be affected by the PPE as well. A failure to maintain a required minimum space between patterns could lead to a leak-current between patterns (and hence directly affect device performance), so it is important that the PPE can be measured accurately. Therefore, we developed a method to measure local PPE in actual device patterns, from CD-SEM images, that also outputs a pattern-contour in which this PPE has been removed. Utilizing such a pattern-contour into the PSQ method enables to quantitatively determine the fidelity of transferred pattern shape solely induced by the 1^st shape modification, while providing PPE data from the device patterns themselves. We believe that a high-quality patterning control (by e.g. optimization of process condition) of MA and MB can be performed only by using such a measurement result. This paper demonstrates and discusses the capability and effectiveness of our newly developed method.

Proceedings Article | 2 April 2014 Paper

SEM-contour shape analysis based on circuit structure for advanced systematic defect inspection

Yasutaka Toyoda, Hiroyuki Shindo, Yutaka Hojo, Daisuke Fuchimoto

Proceedings Volume 9050, 90502V (2014) https://doi.org/10.1117/12.2046655

KEYWORDS: Metals, Shape analysis, Scanning electron microscopy, Inspection, Overlay metrology, Error analysis, Distortion, Defect inspection, Photomasks, Semiconducting wafers

Read Abstract +

Proceedings Article | 10 April 2013 Paper

SEM-contour shape analysis method for advanced semiconductor devices

Yasutaka Toyoda, Hiroyuki Shindo, Yoshihiro Ota, Ryoichi Matsuoka, Yutaka Hojo, Daisuke Fuchimoto, Daisuke Hibino, Hideo Sakai

Proceedings Volume 8681, 86811K (2013) https://doi.org/10.1117/12.2011459

KEYWORDS: Shape analysis, Distortion, Scanning electron microscopy, Edge detection, Semiconductors, Finite element methods, Optical proximity correction, Error analysis, Image analysis, Semiconducting wafers

Read Abstract +

Proceedings Article | 25 September 2010 Paper

Study of shape evaluation for mask and silicon using large field of view

Ryoichi Matsuoka, Hiroaki Mito, Shinichi Shinoda, Yasutaka Toyoda

Proceedings Volume 7823, 78232D (2010) https://doi.org/10.1117/12.867756

KEYWORDS: Photomasks, Silicon, Optical proximity correction, Semiconducting wafers, Metrology, Semiconductors, Bridges, Resolution enhancement technologies, Manufacturing, Design for manufacturing

Read Abstract +

We have developed a highly integrated method of mask and silicon metrology. The aim of this integration is evaluating the performance of the silicon corresponding to Hotspot on a mask. It can use the mask shape of a large field, besides. The method adopts a metrology management system based on DBM (Design Based Metrology). This is the high accurate contouring created by an edge detection algorithm used in mask CD-SEM and silicon CD-SEM. Currently, as semiconductor manufacture moves towards even smaller feature size, this necessitates more aggressive optical proximity correction (OPC) to drive the super-resolution technology (RET). In other words, there is a trade-off between highly precise RET and mask manufacture, and this has a big impact on the semiconductor market that centers on the mask business. As an optimal solution to these issues, we provide a DFM solution that extracts 2-dimensional data for a more realistic and error-free simulation by reproducing accurately the contour of the actual mask, in addition to the simulation results from the mask data. On the other hand, there is roughness in the silicon form made from a mass-production line. Moreover, there is variation in the silicon form. For this reason, quantification of silicon form is important, in order to estimate the performance of a pattern. In order to quantify, the same form is equalized in two dimensions. And the method of evaluating based on the form is popular. In this study, we conducted experiments for averaging method of the pattern (Measurement Based Contouring) as two-dimensional mask and silicon evaluation technique. That is, observation of the identical position of a mask and a silicon was considered. The result proved its detection accuracy and reliability of variability on two-dimensional pattern (mask and silicon) and is adaptable to following fields of mask quality management. •Discrimination of nuisance defects for fine pattern. •Determination of two-dimensional variability of pattern. •Verification of the performance of the pattern of various kinds of Hotspots. In this report, we introduce the experimental results and the application. We expect that the mask measurement and the shape control on mask production will make a huge contribution to mask yield-enhancement and that the DFM solution for mask quality control process will become much more important technology than ever. It is very important to observe the form of the same location of Design, Mask, and Silicon in such a viewpoint. And we report it about algorithm of the image composition in Large Field.

Proceedings Article | 2 April 2010 Paper

Development for 2D pattern quantification method on mask and wafer

Ryoichi Matsuoka, Hiroaki Mito, Yasutaka Toyoda, Zhigang Wang

Proceedings Volume 7638, 76382L (2010) https://doi.org/10.1117/12.846720

KEYWORDS: Photomasks, Silicon, Metrology, Semiconducting wafers, Optical proximity correction, Shape analysis, Semiconductors, Process control, Computer aided design, Inspection

Read Abstract +

We have developed the effective method of mask and silicon 2-dimensional metrology. The aim of this method is evaluating the performance of the silicon corresponding to Hotspot on a mask. The method adopts a metrology management system based on DBM (Design Based Metrology). This is the high accurate contouring created by an edge detection algorithm used in mask CD-SEM and silicon CD-SEM. Currently, as semiconductor manufacture moves towards even smaller feature size, this necessitates more aggressive optical proximity correction (OPC) to drive the super-resolution technology (RET). In other words, there is a trade-off between highly precise RET and mask manufacture, and this has a big impact on the semiconductor market that centers on the mask business. 2-dimensional Shape quantification is important as optimal solution over these problems. Although 1-dimensional shape measurement has been performed by the conventional technique, 2-dimensional shape management is needed in the mass production line under the influence of RET. We developed the technique of analyzing distribution of shape edge performance as the shape management technique. On the other hand, there is roughness in the silicon shape made from a mass-production line. Moreover, there is variation in the silicon shape. For this reason, quantification of silicon shape is important, in order to estimate the performance of a pattern. In order to quantify, the same shape is equalized in two dimensions. And the method of evaluating based on the shape is popular. In this study, we conducted experiments for averaging method of the pattern (Measurement Based Contouring) as two-dimensional mask and silicon evaluation technique. That is, observation of the identical position of a mask and a silicon was considered. It is possible to analyze variability of the edge of the same position with high precision. The result proved its detection accuracy and reliability of variability on two-dimensional pattern (mask and silicon) and is adaptable to following fields of mask quality management. - Estimate of the correlativity of shape variability and a process margin. - Determination of two-dimensional variability of pattern. - Verification of the performance of the pattern of various kinds of Hotspots. In this report, we introduce the experimental results and the application. We expect that the mask measurement and the shape control on mask production will make a huge contribution to mask yield-enhancement and that the DFM solution for mask quality control process will become much more important technology than ever. It is very important to observe the shape of the same location of Design, Mask, and Silicon in such a viewpoint.

Proceedings Article | 13 March 2009 Paper

Integration of mask and silicon metrology in DFM

Ryoichi Matsuoka, Hiroaki Mito, Akiyuki Sugiyama, Yasutaka Toyoda

Proceedings Volume 7275, 72751N (2009) https://doi.org/10.1117/12.816108

KEYWORDS: Photomasks, Silicon, Metrology, Optical proximity correction, Design for manufacturing, Semiconductors, Computer aided design, Lithography, Manufacturing, Edge detection

Read Abstract +

We have developed a highly integrated method of mask and silicon metrology. The method adopts a metrology management system based on DBM (Design Based Metrology). This is the high accurate contouring created by an edge detection algorithm used in mask CD-SEM and silicon CD-SEM. We have inspected the high accuracy, stability and reproducibility in the experiments of integration. The accuracy is comparable with that of the mask and silicon CD-SEM metrology. In this report, we introduce the experimental results and the application. As shrinkage of design rule for semiconductor device advances, OPC (Optical Proximity Correction) goes aggressively dense in RET (Resolution Enhancement Technology). However, from the view point of DFM (Design for Manufacturability), the cost of data process for advanced MDP (Mask Data Preparation) and mask producing is a problem. Such trade-off between RET and mask producing is a big issue in semiconductor market especially in mask business. Seeing silicon device production process, information sharing is not completely organized between design section and production section. Design data created with OPC and MDP should be linked to process control on production. But design data and process control data are optimized independently. Thus, we provided a solution of DFM: advanced integration of mask metrology and silicon metrology. The system we propose here is composed of followings. 1) Design based recipe creation: Specify patterns on the design data for metrology. This step is fully automated since they are interfaced with hot spot coordinate information detected by various verification methods. 2) Design based image acquisition: Acquire the images of mask and silicon automatically by a recipe based on the pattern design of CD-SEM.It is a robust automated step because a wide range of design data is used for the image acquisition. 3) Contour profiling and GDS data generation: An image profiling process is applied to the acquired image based on the profiling method of the field proven CD metrology algorithm. The detected edges are then converted to GDSII format, which is a standard format for a design data, and utilized for various DFM systems such as simulation. Namely, by integrating pattern shapes of mask and silicon formed during a manufacturing process into GDSII format, it makes it possible to bridge highly accurate pattern profile information over to the design field of various EDA systems. These are fully integrated into design data and automated. Bi-directional cross probing between mask data and process control data is allowed by linking them. This method is a solution for total optimization that covers Design, MDP, mask production and silicon device producing. This method therefore is regarded as a strategic DFM approach in the semiconductor metrology.

Proceedings Article | 17 October 2008 Paper

New method of 2-dimensional metrology using mask contouring

Ryoichi Matsuoka, Yoshikazu Yamagata, Akiyuki Sugiyama, Yasutaka Toyoda

Proceedings Volume 7122, 71222T (2008) https://doi.org/10.1117/12.802137

KEYWORDS: Photomasks, Optical proximity correction, Metrology, Semiconductors, Edge detection, Computer aided design, Lithography, Profiling, Inspection, Design for manufacturing

Read Abstract +

Proceedings Article | 30 October 2007 Paper

New method of contour-based mask-shape compiler

Ryoichi Matsuoka, Akiyuki Sugiyama, Akira Onizawa, Hidetoshi Sato, Yasutaka Toyoda

Proceedings Volume 6730, 67300M (2007) https://doi.org/10.1117/12.746559

KEYWORDS: Photomasks, Optical proximity correction, Edge detection, Computer aided design, Scanning electron microscopy, Profiling, Semiconductors, Design for manufacturing, Image processing, Data modeling

Read Abstract +

Proceedings Article | 10 May 2005 Paper

A new matching engine between design layout and SEM image of semiconductor device

Hidetoshi Morokuma, Akiyuki Sugiyama, Yasutaka Toyoda, Wataru Nagatomo, Takumichi Sutani, Ryoichi Matsuoka

Proceedings Volume 5752, (2005) https://doi.org/10.1117/12.602066

KEYWORDS: Scanning electron microscopy, Optical proximity correction, Semiconducting wafers, Image processing, Computer aided design, Edge detection, Image segmentation, Semiconductors, Metrology, Wafer testing

Read Abstract +

Proceedings Article | 21 December 2000 Paper

Extended profile structure with feedback signal based on ICC profile

Tatsuki Inuzuka, Yasutaka Toyoda, Nagaharu Hamada

Proceedings Volume 4300, (2000) https://doi.org/10.1117/12.410775

KEYWORDS: Feedback signals, Printing, Color difference, Color reproduction, Humidity, Temperature metrology, 3D metrology, Data conversion, Signal processing, Standards development

Read Abstract +

Showing 5 of 17 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years