Prediction of critical dimensions for 3D TSV structures using artificial neural network

Jia-Wei Li; Eugene Su; Chao-Ching Ho

doi:10.1117/12.3010132

9 April 2024 Prediction of critical dimensions for 3D TSV structures using artificial neural network

Jia-Wei Li, Eugene Su, Chao-Ching Ho

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129552J (2024) https://doi.org/10.1117/12.3010132
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

TSV (Through-Silicon Via) is a vertical interconnection structure achieved by creating holes in the silicon substrate of a chip. This paper explores the subtle differences in the reflection characteristics of light across various TSV structures. Electromagnetic simulation software, namely, Lumerical FDTD, based on the Finite-Difference Time-Domain (FDTD) method, was employed to establish electromagnetic models for TSV structures. 3D-FDTD simulations calculate near-field electromagnetic field data and derive near-field reflection spectra. The dataset comprises different TSV structures with varying critical dimensions (CD) and their corresponding far-field reflection spectra obtained through simulation. Our approach introduces two deep learning networks: the forward network and the inverse network. Given the time-consuming nature of FDTD electromagnetic simulation tools when dealing with complex structures, the forward network is trained to rapidly predict reflection spectrum signal data corresponding to CD parameters, effectively replacing simulation software. Using a threshold of 1% to determine the accuracy of the spectral predictions, forward prediction achieves 100% accuracy. Similarly, the inverse network is trained to predict the CD parameters of TSV structures from the reflection spectrum signal, allowing for fast and accurate inference of the dimensions of TSV structures and the extraction of geometric parameters. In reverse prediction, the MAPE (Mean Absolute Percentage Error) for R_top, height, and R_bot remains consistently below 5%.

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

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Jia-Wei Li, Eugene Su, and Chao-Ching Ho "Prediction of critical dimensions for 3D TSV structures using artificial neural network", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129552J (9 April 2024); https://doi.org/10.1117/12.3010132

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