Presentation + Paper
10 August 2023 Compressed sensing for optical metrology of semiconductor materials and devices
George Koutsourakis, Andrew Thompson, James C. Blakesley, Aidas Baltusis, Sebastian Wood
Author Affiliations +
Abstract
Local defects and non-uniformities in optoelectronic materials and devices can have an impact on their quality and performance characteristics. The development of non-destructive optical metrology methods that provide spatially resolved information on defects and inhomogeneities is crucial for multiple industries that rely on high quality semiconductor materials and devices, from power electronics and LEDs to solar cells and photodiodes. Traditional point-by-point scanning approaches for microscopy and spectroscopy offer mapping solutions that can produce invaluable datasets, nevertheless in most cases measurements are time-consuming, require complex measurement setups or give very weak signals. In this work we present how a compressed sensing approach can benefit optical metrology techniques and the principles of how to adopt and implement a compressed sensing optical system in practice for semiconductor metrology. As examples, we demonstrate through a simulation process a proposed compressed sensing spectral photoluminescence measurement methodology for characterization of semiconductor materials and devices. The focus in this work is specifically wide bandgap semiconductor materials. The features, advantages and challenges of this compressed sensing optical measurement approach are discussed, including the minimum noise levels required for experimental implementation. Different approaches for reconstruction of the spectral PL datacubes are presented.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
George Koutsourakis, Andrew Thompson, James C. Blakesley, Aidas Baltusis, and Sebastian Wood "Compressed sensing for optical metrology of semiconductor materials and devices", Proc. SPIE 12619, Modeling Aspects in Optical Metrology IX, 1261903 (10 August 2023); https://doi.org/10.1117/12.2673605
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KEYWORDS
Silicon carbide

Compressed sensing

Signal to noise ratio

Image processing

Semiconducting wafers

Reconstruction algorithms

Semiconductor materials

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