Dr. Tetsuaki Matsunawa Profile

Dr. Tetsuaki Matsunawa

Research Engineer at Toshiba Memory Corp

SPIE Involvement:

Author

Publications (20)

Proceedings Article | 23 March 2020 Presentation + Paper

Compact resist model using single convolution kernel

Taiki Kimura, Tetsuaki Matsunawa, Shoji Mimotogi

Proceedings Volume 11327, 113270H (2020) https://doi.org/10.1117/12.2551859

KEYWORDS: Convolution, Photomasks, Lithography, Inverse problems, Statistical modeling

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Proceedings Article | 20 March 2019 Paper

Multi-criteria hotspot detection using pattern classification

Kazufumi Shiozawa, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, Toshiya Kotani

Proceedings Volume 10962, 109620T (2019) https://doi.org/10.1117/12.2515665

KEYWORDS: Semiconducting wafers, Critical dimension metrology, Image classification, Photomasks, System on a chip, Convolution, Wafer inspection

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Proceedings Article | 20 March 2019 Paper

Lithography hotspot candidate detection using coherence map

Tetsuaki Matsunawa, Taiki Kimura, Shigeki Nojima

Proceedings Volume 10962, 109620Q (2019) https://doi.org/10.1117/12.2515664

KEYWORDS: Lithography, Machine learning, Semiconducting wafers, System on a chip, Photomasks, Optical proximity correction, Etching, Manufacturing, Performance modeling, Convolution

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Proceedings Article | 12 October 2018 Presentation

Deep learning in DFM applications (Conference Presentation)

Tetsuaki Matsunawa, Shigeki Nojima

Proceedings Volume 10810, 1081006 (2018) https://doi.org/10.1117/12.2503462

KEYWORDS: Machine learning, Design for manufacturing, Statistical modeling, SRAF, Lithography

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Proceedings Article | 13 July 2017 Paper

Accurate lithography simulation model based on convolutional neural networks

Yuki Watanabe, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima

Proceedings Volume 10454, 104540I (2017) https://doi.org/10.1117/12.2279780

KEYWORDS: Convolutional neural networks, Semiconductor manufacturing, Lithography, Neural networks

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Proceedings Article | 30 March 2017 Presentation + Paper

SOCS based post-layout optimization for multiple patterns with light interference prediction

Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, David Pan

Proceedings Volume 10148, 101480A (2017) https://doi.org/10.1117/12.2260091

KEYWORDS: SRAF, Lithography, Optical lithography, Semiconducting wafers, Wafer-level optics, Photovoltaics, Optical proximity correction, Image processing, Constructive interference, Destructive interference

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Proceedings Article | 30 March 2017 Paper

Accurate lithography simulation model based on convolutional neural networks

Yuki Watanabe, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima

Proceedings Volume 10147, 101470K (2017) https://doi.org/10.1117/12.2257871

KEYWORDS: Convolutional neural networks, Neural networks, Lithography, Optical proximity correction, Feature extraction, Convolution, Design for manufacturing, Semiconducting wafers, Photomasks, Computer architecture, Performance modeling, Critical dimension metrology, Image processing, Data modeling

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SPIE Journal Paper | 21 October 2016

Laplacian eigenmaps- and Bayesian clustering-based layout pattern sampling and its applications to hotspot detection and optical proximity correction

Tetsuaki Matsunawa, Bei Yu, David Pan

JM3, Vol. 15, Issue 04, 043504, (October 2016) https://doi.org/10.1117/12.10.1117/1.JMM.15.4.043504

KEYWORDS: Source mask optimization, Optical proximity correction, Data modeling, Lithography, Feature extraction, Principal component analysis, Model-based design, Performance modeling, Photomasks, Manufacturing

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Proceedings Article | 22 March 2016 Paper

Hybrid hotspot detection using regression model and lithography simulation

Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, David Pan

Proceedings Volume 9781, 97810C (2016) https://doi.org/10.1117/12.2219318

KEYWORDS: Lithography, Computer simulations, 193nm lithography, Feature extraction, Simulation of CCA and DLA aggregates, Semiconducting wafers, Manufacturing, Etching, Scanning electron microscopy, Photomasks

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Proceedings Article | 16 March 2016 Paper

Automatic layout feature extraction for lithography hotspot detection based on deep neural network

Tetsuaki Matsunawa, Shigeki Nojima, Toshiya Kotani

Proceedings Volume 9781, 97810H (2016) https://doi.org/10.1117/12.2217746

KEYWORDS: Neural networks, Feature extraction, Lithography, Machine learning, Data modeling, Optical lithography, Optics manufacturing, Evolutionary algorithms, Detection and tracking algorithms, Design for manufacturing, Denoising, Stochastic processes, Extreme ultraviolet lithography

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SPIE Journal Paper | 11 March 2016

Optical proximity correction with hierarchical Bayes model

Tetsuaki Matsunawa, Bei Yu, David Pan

JM3, Vol. 15, Issue 02, 021009, (March 2016) https://doi.org/10.1117/12.10.1117/1.JMM.15.2.021009

KEYWORDS: Optical proximity correction, Data modeling, Model-based design, Performance modeling, Statistical modeling, Simulation of CCA and DLA aggregates, Lithography, Feature extraction, Monte Carlo methods, Optics manufacturing

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Proceedings Article | 18 March 2015 Paper

A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction

Tetsuaki Matsunawa, Jhih-Rong Gao, Bei Yu, David Pan

Proceedings Volume 9427, 94270S (2015) https://doi.org/10.1117/12.2085790

KEYWORDS: Lithography, Feature extraction, Data modeling, Machine learning, Lawrencium, Manufacturing, Detection and tracking algorithms, Design for manufacturing, Calibration, Performance modeling

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Proceedings Article | 18 March 2015 Paper

Optical proximity correction with hierarchical Bayes model

Tetsuaki Matsunawa, Bei Yu, David Pan

Proceedings Volume 9426, 94260X (2015) https://doi.org/10.1117/12.2085787

KEYWORDS: Optical proximity correction, Data modeling, Model-based design, Statistical modeling, Performance modeling, Feature extraction, Simulation of CCA and DLA aggregates, Lithography, Monte Carlo methods, Statistical analysis

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SPIE Journal Paper | 6 February 2014

Hotspot prevention and detection method using an image-recognition technique based on higher-order local autocorrelation

Hirokazu Nosato, Hidenori Sakanashi, Eiichi Takahashi, Masahiro Murakawa, Tetsuaki Matsunawa, Shimon Maeda, Satoshi Tanaka

JM3, Vol. 13, Issue 01, 011007, (February 2014) https://doi.org/10.1117/12.10.1117/1.JMM.13.1.011007

KEYWORDS: Lithography, Feature extraction, Photomasks, Semiconducting wafers, Design for manufacturing, Data modeling, Semiconductors, Geometrical optics, Binary data, Image classification

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Proceedings Article | 15 March 2012 Paper

Clean pattern matching for full chip verification

Satomi Nakamura, Tetsuaki Matsunawa, Chikaaki Kodama, Takanori Urakami, Nozomu Furuta, Shunsuke Kagaya, Shigeki Nojima, Shinji Miyamoto

Proceedings Volume 8327, 83270T (2012) https://doi.org/10.1117/12.916316

KEYWORDS: Lithography, Data storage, Optical proximity correction, Microelectronics, Design for manufacturability, Current controlled current source, Intellectual property, Time metrology

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Proceedings Article | 13 March 2012 Paper

Generator of predictive verification pattern using vision system based on higher-order local autocorrelation

Tetsuaki Matsunawa, Shimon Maeda, Hirotaka Ichikawa, Shigeki Nojima, Satoshi Tanaka, Shoji Mimotogi, Hirokazu Nosato, Hidenori Sakanashi, Masahiro Murakawa, Eiichi Takahashi

Proceedings Volume 8326, 832615 (2012) https://doi.org/10.1117/12.916306

KEYWORDS: Lithography, Feature extraction, Semiconducting wafers, Resolution enhancement technologies, Statistical analysis, Lithographic illumination, Photomasks, Pattern recognition, Diffraction, Geometrical optics

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Proceedings Article | 5 April 2011 Paper

Hotspot detection using image pattern recognition based on higher-order local auto-correlation

Shimon Maeda, Tetsuaki Matsunawa, Ryuji Ogawa, Hirotaka Ichikawa, Kazuhiro Takahata, Masahiro Miyairi, Toshiya Kotani, Shigeki Nojima, Satoshi Tanaka, Kei Nakagawa, Tamaki Saito, Shoji Mimotogi, Soichi Inoue, Hirokazu Nosato, Hidenori Sakanashi, Takumi Kobayashi, Masahiro Murakawa, Tetsuya Higuchi, Eiichi Takahashi, Nobuyuki Otsu

Proceedings Volume 7974, 79740X (2011) https://doi.org/10.1117/12.881193

KEYWORDS: Lithography, Pattern recognition, Computer aided design, Feature extraction, Intelligence systems, Silicon, Photomasks, Image processing, Visualization, Graphic design

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Proceedings Article | 10 March 2010 Paper

Ultimately accurate SRAF replacement for practical phases using an adaptive search algorithm based on the optimal gradient method

Shimon Maeda, Hirokazu Nosato, Tetsuaki Matsunawa, Masahiro Miyairi, Shigeki Nojima, Satoshi Tanaka, Hidenori Sakanashi, Masahiro Murakawa, Tamaki Saito, Tetsuya Higuchi, Soichi Inoue

Proceedings Volume 7640, 764018 (2010) https://doi.org/10.1117/12.846345

KEYWORDS: SRAF, Lithography, Photomasks, Critical dimension metrology, Algorithm development, Computer simulations, Manufacturing, Image resolution, Optical lithography, Switching

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SRAF (Sub Resolution Assist Feature) technique has been widely used for DOF enhancement. Below 40nm design node, even in the case of using the SRAF technique, the resolution limit is approached due to the use of hyper NA imaging or low k1 lithography conditions especially for the contact layer. As a result, complex layout patterns or random patterns like logic data or intermediate pitch patterns become increasingly sensitive to photo-resist pattern fidelity. This means that the need for more accurate resolution technique is increasing in order to cope with lithographic patterning fidelity issues in low k1 lithography conditions. To face with these issues, new SRAF technique like model based SRAF using an interference map or inverse lithography technique has been proposed. But these approaches don't have enough assurance for accuracy or performance, because the ideal mask generated by these techniques is lost when switching to a manufacturable mask with Manhattan structures. As a result it might be very hard to put these things into practice and production flow. In this paper, we propose the novel method for extremely accurate SRAF placement using an adaptive search algorithm. In this method, the initial position of SRAF is generated by the traditional SRAF placement such as rule based SRAF, and it is adjusted by adaptive algorithm using the evaluation of lithography simulation. This method has three advantages which are preciseness, efficiency and industrial applicability. That is, firstly, the lithography simulation uses actual computational model considering process window, thus our proposed method can precisely adjust the SRAF positions, and consequently we can acquire the best SRAF positions. Secondly, because our adaptive algorithm is based on optimal gradient method, which is very simple algorithm and rectilinear search, the SRAF positions can be adjusted with high efficiency. Thirdly, our proposed method, which utilizes the traditional SRAF placement, is easy to be utilized in the established workflow. These advantages make it possible to give the traditional SRAF placement a new breath of life for low k1.

Proceedings Article | 1 November 2007 Paper

Improving the efficiency of pattern extraction for character projection lithography using OPC optimization

Hirokazu Nosato, Tetsuaki Matsunawa, Hidenori Sakanashi, Masahiro Murakawa, Tetsuya Higuchi

Proceedings Volume 6730, 67304G (2007) https://doi.org/10.1117/12.746687

KEYWORDS: Optical proximity correction, Photomasks, Algorithm development, Logic devices, Optimization (mathematics), Lithography, Projection lithography, Electron beam lithography, Logic, Electron beams

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Proceedings Article | 9 November 2005 Paper

The novel approach for optical proximity correction using genetic algorithms

Tetsuaki Matsunawa, Hirokazu Nosato, Hidenori Sakanashi, Masahiro Murakawa, Nobuharu Murata, Tsuneo Terasawa, Toshihiko Tanaka, Nobuyuki Yoshioka, Osamu Suga, Tetsuya Higuchi

Proceedings Volume 5992, 599254 (2005) https://doi.org/10.1117/12.632041

KEYWORDS: Optical proximity correction, Genetic algorithms, Photomasks, Model-based design, Genetics, Optimization (mathematics), Semiconductors, Semiconducting wafers, Wafer-level optics, Computer simulations

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Showing 5 of 20 publications

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