Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN

Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN

The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a...

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Bibliographic Details
Main Authors: Minji Kang, Sung Kyu Jang, Jihun Kim, Seongho Kim, Changmin Kim, Hyo-Chang Lee, Wooseok Kang, Min Sup Choi, Hyeongkeun Kim, Hyeong-U Kim
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Journal of Sensor and Actuator Networks
Subjects:
plasma diagnostics
reactive ion etching (RIE)
semiconductor manufacturing
machine learning
cycle-consistent adversarial network (CycleGAN)
multi-domain data integration
Online Access:https://www.mdpi.com/2224-2708/13/6/75
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Summary:The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a reactive ion etcher (RIE) system to analyze CF<sub>4</sub>-based plasma. To synchronize and integrate data from these different domains, we developed a Tri-CycleGAN model that utilizes three interconnected CycleGANs for bi-directional data transformation between OES, QMS, and ToF-MS. This configuration enables accurate mapping of data across domains, effectively compensating for the blind spots of individual diagnostic techniques. The model incorporates self-attention mechanisms to address temporal misalignments and a direct loss function to preserve fine-grained features, further enhancing data accuracy. Experimental results show that the Tri-CycleGAN model achieves high consistency in reconstructing plasma measurement data under various conditions. The model’s ability to fuse multi-domain diagnostic data offers a robust solution for plasma monitoring, potentially improving precision, yield, and process control in semiconductor manufacturing. This work lays a foundation for future applications of machine learning-based diagnostic integration in complex plasma environments.
ISSN:2224-2708

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