Machine learning enabled optimization of showerhead design for semiconductor deposition process

Zeqing Jin, Dahyun Daniel Lim, Xueying Zhao, Meenakshi Mamunuru, Sassan Roham and Grace X. Gu ()
Additional contact information
Zeqing Jin: University of California
Dahyun Daniel Lim: University of California
Xueying Zhao: Lam Research Corporation
Meenakshi Mamunuru: Lam Research Corporation
Sassan Roham: Lam Research Corporation
Grace X. Gu: University of California

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 2, No 26, 925-935

Abstract: Abstract In semiconductor fabrication, the deposition process generates layers of materials to realize insulating and conducting functionality. The uniformity of the deposited thin film layers’ thickness is crucial to create high-performance semiconductor devices. Tuning fabrication process parameters (e.g., for evenly distributed gas flow on the semiconductor wafer) is one of the dominant factors that affect film uniformity, as evidenced by both experimental and numerical studies. Conventional trial and error methods employed to change and test a range of fabrication conditions are time-consuming, and few studies have explored the effect of changing the geometry of hardware components, such as the showerhead. Here, we present a design optimization of the showerhead for flow uniformity based on numerical simulation data using machine learning surrogate models. Accurate machine learning models and optimization algorithms are developed and implemented to achieve 10% more flow uniformity compared to a benchmark traditional showerhead design. Moreover, the developed Bayesian optimization method saves 10-fold computational cost in reaching the optimal showerhead designs compared to conventional approaches. This machine learning enabled optimization platform shows promising results which could be implemented for other optimization problems in various manufacturing systems such as semiconductor fabrication and additive manufacturing.

Keywords: Semiconductor deposition process; Showerhead design; Machine learning; Surrogate model; Bayesian optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02082-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-023-02082-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02082-8

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().