Dr. Ali Hallal Profile

Dr. Ali Hallal

Data Scientist at Pollen Metrology

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Publications (5)

Proceedings Article | 10 April 2024 Poster + Paper

Deep learning aided tool for fast and accurate segmentation of multi-part semiconductor features

Isaac Wilfried Sanou, Julien Baderot, Ali Hallal, Stéphanie Bricq, Yannick Benezeth, Franck Marzani, Sergio Martinez, Johann Foucher

Proceedings Volume 12955, 1295523 (2024) https://doi.org/10.1117/12.3009287

KEYWORDS: Image segmentation, Education and training, Deep learning, Performance modeling, Visual process modeling, Contour modeling, Semiconductors, Object detection, Transformers, Image processing

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Proceedings Article | 27 April 2023 Poster + Paper

A generic deep-learning-based defect segmentation model for electron micrographs for automatic defect inspection

Martin Jacob, Ali Hallal, Julien Baderot, Vincent Barra, Arnaud Guillin, Sergio Martinez, Johann Foucher

Proceedings Volume 12496, 124962T (2023) https://doi.org/10.1117/12.2658003

KEYWORDS: Image segmentation, Data modeling, Defect detection, Defect inspection, Photomicroscopy, Object detection, Deep learning, Scanning electron microscopy, Transmission electron microscopy

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Proceedings Article | 26 May 2022 Poster + Paper

Application of machine learning-based metrology for writer main pole CD measurement by CDSEM

Yuan Yang, Anhhuy Ngo, Mingmin Wang, Chester Chien, Julien Baderot, Ali Hallal, Nicolas Clement, Sergio Martinez, Johann Foucher

Proceedings Volume 12053, 120531R (2022) https://doi.org/10.1117/12.2613501

KEYWORDS: Metrology, Performance modeling, Image processing, Data modeling, Machine learning, Head, Semiconducting wafers, Critical dimension metrology, Magnetism, Cadmium

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Critical Dimension measurement in the final slider-level fabrication is essential in the development and manufacture of magnetic read/write heads for hard disk drives (HDD). It validates the device level geometry that plays a dominant role in the magnetic performance of the writer and provides critical feedback to the wafer-level head fabrication process control. Measurement at the slider-level affords the true Air Bearing Surface (ABS) view of the real device that can only be approached by the destructional cross-section at wafer level [1,2]. While the large set of CDSEM images of writer ABS at slider level enables an excellent statistical view of wafer uniformity, it also poses special challenges to the metrology due to a substantial number of variations from the upstream wafer process. The large structure variations observed at the sliderlevel is particularly prevalent in the initial development phase where large DOE (Design of Experiment) are designed to produce intended structure variations, and low process maturity yields large unintended variations among the devices. A traditional metrology used in such a variant data set requires extensive tuning or even a set of separate solutions with each solution in the set only applicable to a small subset of the variations. However, this approach is inefficient and demands high engineer resources. In this work, we use a machine learning based metrology approach to process the large set of magnetic writer device images at the slider level. For the current study, we use a model-based solution that was trained with deep learning (DL) using a dataset from 4 different head designs. The model aims to retrieve precise boundaries of the head to perform accurate measurements. We demonstrate the progressive robustness of the model-based solution by expanding the training set to measure the CD of writer poles with different designs and large process variations due to the intrinsic wafer level structure variation and the image distortions from the slider fabrication process. In addition, we will demonstrate the efficiency of the Deep Learning (DL) based solution in comparison with traditional metrology and manual measurements on the same set of data.

Proceedings Article | 20 August 2020 Presentation

Germanium: a semiconducting platform for spin-orbitronics

Thomas Guillet, Carlo Zucchetti, Adele Marchionni, Quentin Barbedienne, Ali Hallal, Alain Marty, Céline Vergnaud, Giovanni Isella, Henri Jaffrès, Nicolas Reyren, Jean-Marie George, Mair Chshiev, Marco Finazzi, Franco Ciccacci, Paolo Biagioni, Aurélien Masseboeuf, Hanako Okuno, Federico Bottegoni, Albert Fert, Matthieu Jamet

Proceedings Volume 11470, 114702Q (2020) https://doi.org/10.1117/12.2567222

KEYWORDS: Germanium, Semiconductors, Optical properties, Spintronics, Transistors, Silicon, Nanotechnology, Dielectrics, Magnetism

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Proceedings Article | 16 September 2011 Paper

Influence of lattice relaxation on the electron-spin motion in ferromagnetic films: experiment and theory

T. Berdot, A. Hallal, P. Dey, L. Tati Bismaths, L. Joly, A. Bourzami, H. Bulou, F. Scheurer, J. Henk, M. Alouani, W. Weber

Proceedings Volume 8100, 81000Z (2011) https://doi.org/10.1117/12.893320

KEYWORDS: Iron, Reflection, Interfaces, Scattering, Spin polarization, Silver, Electron beams, Annealing, Thin films, Ferromagnetics

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