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Predicting the Critical Dimensions of Micron and Sub-micron Structures Using Joint Training Models and Electromagnetic Simulation Tools

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Abstract

This paper presents an enhanced non-destructive optical measurement method crucial for 3D IC technology. Utilizing a deep neural network and electromagnetic simulation tool, an inverse model is established to predict the critical dimensions (CD) of micron and sub-micron structures. The forward model accelerates data generation, overcoming limitations of the finite-difference time-domain (FDTD) method. Compared to FDTD simulations of reflectance spectra which take about 50 min, the model's prediction time for reflectance spectra is only 0.01 s. The three key dimensions with better prediction results from the inverse model are top radius (Rtop), height, and bottom radius (Rbot). In the proposed joint training model, by simultaneously training forward and inverse models and adjusting the weights of forward and inverse loss functions, the mean absolute percentage error (MAPE) for Rtop, height, and Rbot is reduced from 0.26 to 0.08%, 0.69 to 0.44%, and 4.01 to 0.6% respectively.

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Funding

This study was supported by National Science and Technology Council (Grant No. 112-2218-E-027-008).

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Authors and Affiliations

  1. Graduate Institute of Manufacturing Technology and Department of Mechanical Engineering, National Taipei University of Technology, Taipei City, Taiwan

    Jia-Wei Li, Chong-Han Hsu, Jiao-Kai Wang, Bo-En Tsai, Eugene Su & Chao-Ching Ho

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  1. Jia-Wei Li
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  2. Chong-Han Hsu
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  3. Jiao-Kai Wang
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  4. Bo-En Tsai
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  5. Eugene Su
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Correspondence to Chao-Ching Ho.

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This paper was presented at ISMTII2023.

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Li, JW., Hsu, CH., Wang, JK. et al. Predicting the Critical Dimensions of Micron and Sub-micron Structures Using Joint Training Models and Electromagnetic Simulation Tools. Int. J. Precis. Eng. Manuf. (2024). https://doi.org/10.1007/s12541-024-00981-1

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  • DOI: https://doi.org/10.1007/s12541-024-00981-1

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