Dr. Dorin Cerbu Profile

Dr. Dorin Cerbu

at imec

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Publications (10)

Proceedings Article | 10 April 2024 Presentation + Paper

Detection of crystalline defects in Si/SiGe superlattices towards 3D-DRAM applications

M. Beggiato, D. Cerbu, R. Loo, W. Sun, A. Moussa, G. Bast, K. Fukaya, C. Beral, A.-L. Charley, N. Janardan, A. Cross, G. Lorusso, M. Isawa, A. Belmonte, G. Sankar Kar, J. Bogdanowicz

Proceedings Volume 12955, 129551F (2024) https://doi.org/10.1117/12.3011279

KEYWORDS: Semiconducting wafers, Optical inspection, Inspection, Crystals, Sampling rates, Laser phosphor displays, Superlattices, Optical testing, Defect detection, Signal processing

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Si/SiGe heterostructures are gaining traction as a starting template in applications such as Gate-All-Around Field-Effect Transistor (GAAFET), complementary FET (CFET), and 3-dimensional dynamic random access memory (3D-DRAM), where the SiGe alloy plays the role of sacrificial material for channel release. However, the formation of crystalline defects (e.g. crosshatch) in the epitaxially grown layers plays a critical part in determining the overall device performance. As such, it is key to be able to control the defectivity level using large surface area inspection techniques. The challenge of such inspection is that it must combine a high enough throughput to detect low-density defects together with sensitivity to nanometer size defects. In addition, the technique should also be able to distinguish these elongated one-dimensional crystalline defects from other types of defects. In this study, we investigate the impact of the number of Si/SiGe bilayers on the crystal defect distribution utilizing a combined approach of optical inspection and extensive e-beam review for both qualitative and quantitative defect characterization. In-line optical inspection techniques revealed that the crosshatch density and distribution varied significantly with the number of Si/SiGe bilayers. These observations were then confirmed by high-resolution e-beam review coupled with image analysis and signal processing to enable crosshatch quantification. Our approach considers an initial investigation on thin Si/SiGe bilayers (up to ~5x bilayers) and is further extended to thick stacks (up to 60x bilayers) to evaluate the capability of optical inspection as the high-throughput reference technique. In conclusion, this study aims to develop a methodology to investigate the crosshatch density in Si/SiGe superlattices, using optical inspection and e-beam review as main characterization tools. These techniques offer valuable insights in terms of defect distribution at the wafer level for the design and fabrication of next-generation semiconductor devices.

Proceedings Article | 10 April 2024 Presentation + Paper

Machine learning methods for voltage contrast yield analysis

D. Cerbu, V. Blanco Carballo, F. Schleicher, J. van de Kerkhove, P. Leray, N. Kissoon, E. De Poortere

Proceedings Volume 12955, 129551C (2024) https://doi.org/10.1117/12.3011142

KEYWORDS: Semiconducting wafers, Machine learning, Image processing, Design, Scanning electron microscopy

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SPIE Journal Paper | 19 October 2023

Voltage contrast determination of design rules at the limits of EUV single patterning

Victor Blanco Carballo, Etienne De Poortere, Philippe Leray, Dorin Cerbu, Jeroen van de Kerkhove, Nicola Kissoon

JM3, Vol. 22, Issue 04, 041604, (October 2023) https://doi.org/10.1117/12.10.1117/1.JMM.22.4.041604

KEYWORDS: Metals, Design rules, Design, Critical dimension metrology, Scanning electron microscopy, Diffractive optical elements, Extreme ultraviolet, Etching, Optical lithography, Transmission electron microscopy

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Proceedings Article | 28 April 2023 Presentation + Paper

Frequency-informed deep-learning denoising method supporting sub-nm metrology for high NA EUV lithography

Minjung Kim, Dorin Cerbu, Selim Dogru, Kumara Sastry, Gian Lorusso, Mohamed Zidan, Mohamed Saib, Joren Severi, Danilo De Simone, Vivek Singh

Proceedings Volume 12495, 124951D (2023) https://doi.org/10.1117/12.2661138

KEYWORDS: Extreme ultraviolet lithography, Photoresist technology, Line edge roughness, Critical dimension metrology, Critical dimension scanning electron microscopy, Image processing, Signal to noise ratio, Denoising, Scanning electron microscopy, Metrology, Line width roughness, Artificial intelligence, Machine learning

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Proceedings Article | 28 April 2023 Paper

Improved methodology for prediction of merged contact hole defect process window

Andreas Frommhold, Dorin Cerbu, Gijsbert Rispens, Mark John Maslow, Paulina Rincon Delgadillo

Proceedings Volume 12494, 124940L (2023) https://doi.org/10.1117/12.2658306

KEYWORDS: Critical dimension metrology, Failure analysis, Random forests, Stochastic processes, Statistical analysis, Scanning electron microscopy

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Proceedings Article | 31 May 2022 Presentation + Paper

Deep learning-enabled vertical drift artefact correction for AFM images

Dorin Cerbu, Kristof Paredis, Alain Moussa, Anne-Laure Charley, Philippe Leray

Proceedings Volume PC12053, PC120530L (2022) https://doi.org/10.1117/12.2614029

KEYWORDS: Atomic force microscopy, Neural networks, Metrology, Image processing, Convolution, Line scan image sensors, Scanning probe microscopy, Semiconductors

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Proceedings Article | 23 March 2020 Paper

Unsupervised machine learning based CD-SEM image segregator for OPC and process window estimation

Bappaditya Dey, Dorin Cerbu, Kasem Khalil, Sandip Halder, Philippe Leray, Sayantan Das, Yasser Sherazi, Magdy Bayoumi, Ryoung Han Kim

Proceedings Volume 11328, 113281G (2020) https://doi.org/10.1117/12.2552055

KEYWORDS: Image processing, Optical proximity correction, Machine learning, Computer programming, Photomasks, Stochastic processes, Defect inspection, Semiconducting wafers, Analytical research, Vector spaces

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Proceedings Article | 29 November 2019 Presentation + Paper

Predicting stochastic defects across the process window

Andreas Frommhold, Dorin Cerbu, Joost Bekaert, Lieve Van Look, Mark Maslow, Gijsbert Rispens, Eric Hendrickx

Proceedings Volume 11147, 1114708 (2019) https://doi.org/10.1117/12.2536898

KEYWORDS: Critical dimension metrology, Etching, Stochastic processes, Failure analysis, Optical correlators, Cadmium, Finite element methods, Inspection, Metrology, Defect detection

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Proceedings Article | 26 March 2019 Presentation + Paper

Impact of local variability on defect-aware process windows

Mark John Maslow, Hidetami Yaegashi, Andreas Frommhold, Guido Schiffelers, Felix Wahlisch, Gijsbert Rispens, Bram Slachter, Keisuke Yoshida, Arisa Hara, Noriaki Oikawa, Abhinav Pathak, Dorin Cerbu, Eric Hendrickx, Joost Bekaert

Proceedings Volume 10957, 109570H (2019) https://doi.org/10.1117/12.2514719

KEYWORDS: Critical dimension metrology, Etching, Metrology, Lithography, Semiconducting wafers, Photoresist processing, Statistical analysis, Optical lithography, Stochastic processes

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Proceedings Article | 26 March 2019 Presentation

Deep-learning-based SEM image denoiser (Conference Presentation)

Dorin Cerbu, Sandip Halder, Philippe Leray

Proceedings Volume 10959, 1095916 (2019) https://doi.org/10.1117/12.2515182

KEYWORDS: Scanning electron microscopy, Stochastic processes, Image quality, Extreme ultraviolet, Algorithm development, Image processing, Inspection, Photomask technology, Extreme ultraviolet lithography, Artificial neural networks

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Deep-learning-based SEM image denoiser Dorin Cerbu1, Sandip Halder1, Philippe Leray1 1IMEC, Kapeldreef 75, B-3001 Leuven, Belgium We report the development of a new method to denoise SEM images with the help of artificial neural networks. Upon using a preprocessing and training scheme tailored for SEM images of structures, most often encountered in semiconductor manufacturing, we can efficiently denoise images affected with varying degrees of noise severity and origin. In the figure below, we show an example of how we can use this filter efficiently to treat noisy images and improve the image quality. This can help in acquisition of more stable and better metrology data. Fig1(a) original image (b) Image which has been denoised using deep-learning based algorithms This development is of utmost importance for the case of post-litho processing step where resist nanostructures when SEM inspected are usually impacted by the electron beam and shrink, hence skewing critical dimension measurements. This is especially true as we push towards sub N-10 nm nodes. Application of our deep-learning processing scheme allows efficient noise reduction on SEM inspection images and helps us discern minor details previously shadowed by noise. This is extremely important as we move towards using EUV in high volume manufacturing. Small details can be crucial to understand the root-cause of stochastic and process defects. In previous work, we have already shown different approaches to understand stochastic defects [1-2]. The goal of this work is to enhance the image quality as much as possible to gain further fundamental understanding on nano-defects. [1] S. Halder et. al., ‘Using machine learning techniques to understand EUV stochastics, SPIE Photomask Technology + Extreme Ultraviolet Lithography, 2018 [2] K. Sah et.al., ‘EUV stochastic defect monitoring with advanced Broadband optical wafer inspection and e-Beam review systems’, SPIE Photomask Technology + Extreme Ultraviolet Lithography, 2018

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