Dr. Tsuyoshi Kondo Profile

Dr. Tsuyoshi Kondo

at Hitachi High-Tech Corp

SPIE Involvement:

Author

Publications (7)

Proceedings Article | 27 April 2023 Presentation + Paper

Dry resist metrology readiness for high-NA EUVL

Gian Francesco Lorusso, Dieter Van Den Heuvel, Mohamed Zidan, Alain Moussa, Christophe Beral, Anne-Laure Charley, Danilo De Simone, Anuja De Silva, Elisseos Verveniotis, Ali Haider, Tsuyoshi Kondo, Hiroyuki Shindo, Yasushi Ebizuka, Miki Isawa

Proceedings Volume 12496, 1249612 (2023) https://doi.org/10.1117/12.2658280

KEYWORDS: Fourier transforms, Signal to noise ratio, Bridges, Inspection, Printing, Metrology, Film thickness, Extreme ultraviolet lithography, Atomic force microscopy, Defect detection

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Proceedings Article | 13 June 2022 Presentation

Detection and monitoring of defects/anomalies in random circuit patterns using autoencoder

Hiroshi Fukuda, Tsuyoshi Kondo

Proceedings Volume PC12053, PC120530E (2022) https://doi.org/10.1117/12.2611002

KEYWORDS: Scanning electron microscopy, Inspection, Visualization, Signal to noise ratio, Particles, Optical lithography, Logic, Integrated circuits, Image processing, Image acquisition

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Proceedings Article | 26 May 2022 Presentation + Paper

Metrology of thin resist for high NA EUVL

Gian Francesco Lorusso, Christophe Beral, Janusz Bogdanowicz, Danilo De Simone, Mahmudul Hasan, Christiane Jehoul, Alain Moussa, Mohamed Saib, Mohamed Zidan, Joren Severi, Vincent Truffert, Dieter Van den Heuvel, Alex Goldenshtein, Kevin Houchens, Gaetano Santoro, Daniel Fischer, Angelika Muellender, Joey Hung, Roy Koret, Igor Turovets, Kit Ausschnitt, Chris Mack, Tsuyoshi Kondo, Tomoyasu Shohjoh, Masami Ikota, Anne-Laure Charley, Philippe Leray

Proceedings Volume 12053, 120530O (2022) https://doi.org/10.1117/12.2614046

KEYWORDS: Fourier transforms, Signal to noise ratio, Metrology, Line width roughness, Scanning electron microscopy, Semiconducting wafers, Atomic force microscopy, Scatterometry, Extreme ultraviolet lithography, Image quality

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One of the many constrains of High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL) is related to resist thickness. In fact, one of the consequences of moving from current 0.33NA to 0.55NA (high NA) is the Depth of Focus (DOF) reduction. In addition, as the resist feature lines shrink down to 8nm half pitch, it is essential to limit the aspect ratio to avoid pattern collapse. The direct consequence of such a situation is that a resist thickness of 30nm, usually used for 32nm pitch dense line/space (LS), will not be suitable for 16nm pitch, where the target thickness is expected to be 15nm thickness or less to ensure a similar aspect ratio. The question we need to answer is how the resist thickness reduction will impact the various metrology techniques needed to properly set up a process. To address this question, a set of wafers using both Chemical Amplified Resist (CAR) and Metal Oxide Resist (MOR) at different thicknesses and with different types of underlayer have been generated for LS patterns at 32nm pitch. We first investigated the impact of film thickness by scanning electron microscope (SEM) on the imaging of CAR resist lines. To start with, our current Best-Known Methods (BKM’s) were used to acquire the SEM images. As resist thickness decreases, noise level and image contrast are observed to degrade dramatically. Such an image quality degradation may directly impact the quality of the CD measurements both in terms of accuracy and precision. In this paper we investigated the thin resist wafer set described above using various techniques, such as Critical Dimension Scanning Electron Microscope (CD SEM), Atomic Force Microscopy (AFM), Low Voltage SEM (LV SEM), scatterometry, Pattern Shift Response (PSR), and optical defect inspection. The impact of the resist thickness is estimated for each approach, and optimal settings were investigated to minimize the relative impact on metrology. Our results indicated that, in most cases, alternative operation conditions and BKM settings, sometimes drastically different from the usual operation condition, must be used to guarantee the metrology requirements. Our results show that, despite the impact of thinning resist materials, it is possible to find appropriate settings to strengthen the metrology quality output.

Proceedings Article | 26 May 2022 Presentation + Paper

Regularized autoencoder for the analysis of multivariate metrology data

Mohamed Saib, Gian Francesco Lorusso, Anne-Laure Charley, Philippe Leray, Tsuyoshi Kondo, Hiroyuki Shindo, Yasushi Ebizuka, Naoma Ban, Masami Ikota

Proceedings Volume 12053, 120530V (2022) https://doi.org/10.1117/12.2613729

KEYWORDS: Data modeling, Principal component analysis, Metrology, Critical dimension metrology, Computer programming, Manufacturing, Data analysis, Scanning electron microscopy, Semiconducting wafers, Machine learning

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A new generation of metrology tools has been recently released to the market, allowing extensive characterization of semiconductor samples thanks to massive measurements. The resulting substantial growth of measurement sets in size and number of descriptors made the data analysis with the traditional statistical techniques laborious, being unable to consider all data simultaneously. In this work, we propose an analysis method applicable to the massive measurement sets based on a machine learning technique, called Autoencoder (AE). The performance of the original AE model has been boosted in this work thanks to its regularization, by imposing orthogonality of its internal representation and by training only half of the model weights while the second half is deduced. Practically, once the model is trained on the data set of interest, it is used to study relationships between input variables through the chart of the circle of correlations. In this chart, input variables are projected as vectors into the compression plane where the angular distance between them will express their degree of correlation. The representation of massive measurements data through a condensed simple chart that still shows complex interactions between variables is in fact very efficient and facilitating the interpretation of measurements themselves. The experimental validation of the proposed method has been done with measurements of Contact Hole (CH) patterns acquired on samples manufactured with 3 different exposure conditions (underexposure dose, nominal exposure dose, and overexposure dose). The proposed data analysis technique allowed us to clearly identify the impact of the process conditions on patterns characteristics, such as their eccentricity, their geometry, and more. Our results indicated that in case of overexposure, an anisotropic distortion of the CH geometry is present, where 𝑌-axis is the major axis, with larger impact on the pattern surface as compared to the 𝑋-axis. This result has been also confirmed later through defect inspection, where we found that 94% of bridging defects detected occurred along the 𝑌-axis, while only 6% of bridging occurred along the 𝑋-axis. In conclusion, our results indicated that the proposed analysis technique is highly effective for the analysis and interpretation of massive metrology datasets, and that the dimensionality increase of the dataset has a negligible impact on this technique's performance.

SPIE Journal Paper | 5 November 2021 Open Access

Anomaly detection in random circuit patterns using autoencoder

Hiroshi Fukuda, Tsuyoshi Kondo

JM3, Vol. 20, Issue 04, 044001, (November 2021) https://doi.org/10.1117/12.10.1117/1.JMM.20.4.044001

KEYWORDS: Inspection, Scanning electron microscopy, Logic, Defect detection, Image processing, Signal to noise ratio, Visualization, Stochastic processes, Optical lithography, Computer programming

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Proceedings Article | 22 February 2021 Presentation + Paper

Multivariate analysis methodology for the study of massive multidimensional SEM data

Mohamed Saib, Gian Francesco Lorusso, Anne-Laure Charley, Philippe Leray, Tsuyoshi Kondo, Yuta Kawamoto, Yasushi Ebizuka, Naoma Ban

Proceedings Volume 11611, 116112C (2021) https://doi.org/10.1117/12.2583696

KEYWORDS: Semiconducting wafers, Scanning electron microscopy, Metrology, Finite element methods, Extreme ultraviolet lithography, Data analysis, Structural design, Semiconductors, Process control, Photoresist processing

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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

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