Mr. Ben Hsieh Profile

Ben Hsieh

at KLA Taiwan

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Proceedings Article | 27 April 2023 Poster + Paper

Mueller matrix spectroscopy and physics-based machine learning for gate-all-around sheet-specific metrology

Houssam Chouaib, Anderson Chou, Valeria Dimastrodonato, Shawn Lin, Ben Hsieh, HaoMiao Chang, James Chuang, Brooks Hsiao, Stilian Pandev, Zhengquan Tan, Derrick Shaughnessy

Proceedings Volume 12496, 124962V (2023) https://doi.org/10.1117/12.2658085

KEYWORDS: Education and training, Gallium arsenide, Metrology, Nanosheets, Data modeling, Etching, Silicon, 3D modeling, Mueller matrices, Scatterometry

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The complex vertically stacked gate-all-around (GAA) manufacturing process drives the demand for more challenging inline metrology requirements. GAA technology with specific technical requirements starts from the first process step, 1) the superlattice, where the multi-stack Si/SiGe pairs must be grown defect-free with matched Si nanosheet thicknesses, and %Ge per layer, sharp interfaces, and a minimized subsequent thermal Ge diffusion across the stacks. More critical steps, among others, are the 2) partial recess of the sacrificial SiGe layers that precede 3) the inner spacers which prevent a channel to source/drain short circuit and reduce the parasitic capacitance, and 4) the channel release process at the “remove poly gate” module, where the SiGe is selectively removed before the high-k metal gate. Driven by tight performance control, a sheet-specific metrology solution is highly desired at each of the above four critical steps. The ideal solution for such an application is non-destructive, precise, accurate, and highly productive. In this paper, a scatterometry critical dimension (SCD) solution for the GAA sheet-specific measurement from various GAA structures is presented. The SCD solution includes an advanced and optimized full Mueller Matrix spectroscopic ellipsometry in conjunction with a physics-assisted machine learning (ML) algorithm. Additionally, the best methodology to address the solution's robustness to process variation is described and presented. It will be shown that an optimized signal-to-noise ratio combined with ML can provide a superior optical metrology solution to the growing challenge in GAA applications.

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