Mr. Anhhuy Ngo Profile

Anhhuy Ngo

at Western Digital Corp

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Publications (3)

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 | 22 February 2021 Poster + Presentation + Paper

Introducing machine learning-based application for writer main pole CD metrology by dual beam FIB/SEM

Ardavan Zandiatashbar, Anhhuy Ngo, Chester Chien, Julien Baderot, Sergio Martinez, Bertrand Darbon, Johann Foucher

Proceedings Volume 11611, 116112V (2021) https://doi.org/10.1117/12.2583746

KEYWORDS: Metrology, Machine learning, Machine vision, Critical dimension metrology, Scanning electron microscopy, 3D metrology, Semiconducting wafers, Image processing, High volume manufacturing, 3D image processing

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Proceedings Article | 19 March 2018 Presentation

High repeatability CD-SEM metrology of disk drive writer and extraction of SWA (Conference Presentation)

Narender Rana, Anhhuy Ngo, Chester Chien, Sergey Babin, Igor Ivonin

Proceedings Volume 10585, 1058516 (2018) https://doi.org/10.1117/12.2301328

KEYWORDS: Metrology, Critical dimension metrology, Scanning electron microscopy, Semiconducting wafers, Image processing software, Semiconductor manufacturing, 3D metrology, Process control, Transmission electron microscopy, Manufacturing

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In contrast to semiconductor manufacturing where features are mostly lines or contact holes, the disk drive reader has a complex, nonlinear 3D geometry. Metrology of such geometries is challenging; especially with regard to repeatability of measurements. New methods were needed to keep up with production requirements for metrology regarding uncertainty of critical dimensions (CD). We report a new method developed for CD metrology of the disk drive writer pole. The method demonstrated improved uncertainties compared to the regularly used CD-SEM algorithms and also has capability for side wall angle (SWA) metrology for process control. The method utilizes multiple steps: a) extract contours from SEM images, b) identify exact locations on a curvilinear feature where CD should be measured, and c) provide CD measurements at these locations. SEM images from a variety of production wafers were used for evaluation of the developed method. Multiple series of SEM images were processed using a software utilizing advanced algorithms without using regular brightness threshold CD-SEM methodologies. It was found that CD repeatability was improved by a factor of three compared to the results of the regular threshold based CD-SEM method. TEM imaging is used to measure side wall angle; it takes a lot of efforts for sample preparation and the feedback is slow. If extraction of SWA is possible from top down SEM images it would provide instant feedback to manufacturing and reduce cost. Monte Carlo simulations were used to understand the sensitivity of SWA to the trench CD and depth. Height of features was measured using AFM. A method to extract SWA from top down images was developed. Numerous SEM images were processed; results were compared to experiment and analyzed. It was shown that the 3-sigma repeatability of SWA measurement was 0.15 degree. It was also found that the left and the right SWA were different on multiple wafers, the results were very consistent from one image series to another one, at the same time the SWA difference between the left and right walls was considerably larger than the uncertainty of SWA measurement.

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