Integration of deep learning for nonlinear spectral decomposition of in situ interfaces analysis

Jingyu Chen; Puzhen Li; Yudan Su; Weixuan Zeng; Shisheng Xiong

doi:10.1117/12.3055530

10 December 2024 Integration of deep learning for nonlinear spectral decomposition of in situ interfaces analysis

Jingyu Chen, Puzhen Li, Yudan Su, Weixuan Zeng, Shisheng Xiong

Author Affiliations +

Proceedings Volume 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024); 134231P (2024) https://doi.org/10.1117/12.3055530
Event: 8th International Workshop on Advanced Patterning Solutions (IWAPS 2024), 2024, Jiaxing, Zhejiang, China

Abstract

Extreme Ultraviolet Lithography (EUV) and other advanced manufacturing technologies have increased the demand for characterizing 3D integrated structures. Due to the inability to directly access and observe buried interfaces, existing metrology tools face significant challenges in this area. Sum Frequency Generation (SFG) spectroscopy, with its surface and interface selectivity, non-destructive nature, and high sensitivity, represents a feasible option for probing molecular interactions at these buried interfaces. However, the nonlinear characteristics of SFG spectra and the coupling between spectral components make manual spectral decomposition highly complex, requiring extensive experience in spectral analysis and is relatively time-consuming, while the stability can hardly be ensured. This severely limits the application of SFG in large-scale, high-throughput characterizations. To overcome this bottleneck, we have developed a toolkit for the decomposition process by integrating advanced deep learning techniques, specifically a Multi-Layer Perceptron (MLP) network with custom activation functions. This toolkit reduces the analysis time from several hours to just a few minutes, while maintaining high accuracy compared to manual operations.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingyu Chen, Puzhen Li, Yudan Su, Weixuan Zeng, and Shisheng Xiong "Integration of deep learning for nonlinear spectral decomposition of in situ interfaces analysis", Proc. SPIE 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024), 134231P (10 December 2024); https://doi.org/10.1117/12.3055530

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KEYWORDS

Sum frequency generation

Interfaces

Deep learning

Optical surfaces

Polystyrene

Signal attenuation

Signal intensity

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