Presentation + Paper
15 August 2023 AI-guided numerical-aperture-controlled scatterometry for measurement of deep HAR and thin-film structures with a large depth variation
Author Affiliations +
Abstract
This research means to solve the challenges in measuring deep high-aspect-ratio (HAR) and thin-film structures in 3D integrated circuits. As the semiconductor industry reaches its physical limitations in device scaling, advanced technologies such as advanced lithography and packaging have become crucial in extending Moore's law. This has led to the use of denser nano-to-sub-micron structures in three-dimensional integrated circuits (3D-IC), resulting in smaller, more functional devices. However, measuring these complex and deep HAR and thin-film structures with a large depth range from a few nanometers to a few hundred micrometers using a single optical system is challenging. To address this need, this article presents an AI-guided scatterometry method using numerical aperture control to achieve a large measurement range. The system uses broadband light to generate multi-wavelength reflection responses from the samples. With the help of an electromagnetic simulation tool and an artificial neural network model, the depth resolution can be improved through inverse modeling. The results demonstrate the ability to measure a wide range of samples with depths ranging from nanometers to micrometers scale, including sub-micron HAR openings and ultra-thin films, as long as the measurement bias is controlled within acceptable limits.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zih-Ying Fu, Fu-Sheng Yang, Min-Ru Wu, Yen-Hung Hung, and Liang-Chia Chen "AI-guided numerical-aperture-controlled scatterometry for measurement of deep HAR and thin-film structures with a large depth variation", Proc. SPIE 12618, Optical Measurement Systems for Industrial Inspection XIII, 1261805 (15 August 2023); https://doi.org/10.1117/12.2673608
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KEYWORDS
Reflectivity

Thin films

Film thickness

Scatterometry

Polarization

Artificial neural networks

Spectroscopy

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