Seminar & Colloquium
[세미나: 7월 5일(금), 오전 10시 30분] Prof. Byungjo Kim, UNIST
Title
Multiscale Design for Industrial Semiconductor Processing with Computational Science and Artificial Intelligence
Speaker
Prof. Byungjo Kim, Graduate School of Semiconductor Materials and Devices Engineering, UNIST(Ulsan National Institute of Science and Technology)
* Education
- 2012. 08. ~ 2019. 02. Ph. D., School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Republic of Korea (Integrated M.S.-Ph.D. course)
Thesis: Multiscale Study on Viscoelastic and Hysteresis Friction Behavior of Elastomer
Advisor: Prof. Maenghyo Cho
- 2004. 03. ~ 2011. 02. B.S., Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea
* Professional Experience
- 2024. 01. ~ Current Assistant Professor, Graduate School of Semiconductor Materials and Devices Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- 2019. 03. ~ 2023. 12. Staff Engineer, Mechatronics Research, Samsung Electronics Co., Ltd.
| Date | Friday, July 5th , 2024
| Time | 10:30 ~
| Venue | 33동 330호
[Abstract]
In this seminar, we'll dive into the cutting-edge developments in semiconductor manufacturing, focusing on the role of computational science and artificial intelligence in enhancing our understanding and control over the manufacturing process. We'll discuss how simulations are key to unraveling the complex interactions that occur at the surface during processing, particularly as devices become smaller and more complex.
A multiscale strategy, integrating Molecular Dynamics and Density Functional Theory, serves as the foundation for probing atomic-level interactions. This detailed analysis helps inform broader computational models, improving our understanding of how features develop on the semiconductor surface and the overall behavior of the wafer.
The presentation will further examine the impact of process adjustments on plasma characteristics and the surfaces under modification. This understanding is crucial for adjusting the material properties and the shapes of the patterns on the semiconductor. Additionally, we'll highlight how integrating simulation techniques with machine learning can lead to more precise control over these patterns and properties, pushing the boundaries of what's possible in semiconductor manufacturing.
[1] J. Trieschmann, L. Vialetto, T. Gergs, J. Micro/Nanopattern. Mats. Metro 22, 4 (2023)
[2] B. Kim, J. Bae, H. Jeong, S. H. Hahn, S. Yoo, S. K. Nam, J. Phys. D: Appl. Phys. 56, 384005 (2023).
[3] B. Kim, M. Kim, S. Yoo, S. K. Nam, Appl. Surf. Sci. 593, 153297 (2022)
[4] H. Jeong, B. Kim, S. Yoo, S. K. Nam, International Conference on Simulation of Semiconductor Processes and Devices (2023)
| Host | 한승우 교수(02-880-7088)