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Recent Innovations in Plasma Sensing and Diagnosis Technology
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Recent Innovations in Plasma Sensing and Diagnosis Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 2895

Special Issue Editor

Dr. Hyeong-U Kim

E-Mail Website
Guest Editor
Department of Plasma Engineering, Korea Institute of Machinery and Materials (KIMM), Daejeon 34103, Republic of Korea
Interests: plasma; 2D materials; synthesis; etch; diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Recent advancements in plasma sensing and diagnosis technology have had a significant impact on the semiconductor industry. Plasma-based techniques, such as plasma etching and deposition, have become essential in the manufacturing of advanced, high-performance semiconductor devices that consume less power. Additionally, sensing and diagnosis technology plays a critical role in ensuring the quality and reliability of semiconductor products by detecting defects, contamination, and process variations.

I am pleased to announce a Special Issue on plasma engineering-based sensing and diagnosis technology. The aim of this issue is to bring together original research and reviews covering plasma synthesis, deposition, and etching, with a focus on enhancing the development of semiconductor devices, enabling the fabrication of more complex and functional structures, and improving the manufacturing process yield. Furthermore, the application of plasma sensing and diagnosis technology has the potential to drive the development of new technologies and applications, such as flexible electronics, the Internet of Things (IoT), and artificial intelligence (AI). 

Furthermore, the use of machine learning in sensing and diagnosing plasma processes is becoming increasingly popular due to its ability to handle large amounts of data. By analyzing the collected data and extracting relevant features, machine learning algorithms can predict whether the plasma process is normal or abnormal, leading to more accurate diagnoses than traditional methods can provide.

We invite researchers in the field of plasma sensing and diagnosis technology to contribute to this Special Issue. We believe that this issue will provide a valuable platform for discussing the latest research findings and future directions for these technologies in the semiconductor industry.

Dr. Hyeong-U Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • plasma
  • synthesis
  • deposition
  • etching
  • machine learning
  • 2D materials
  • sensor
  • diagnosis
  • probe
  • optic

Published Papers (2 papers)

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Research

12 pages, 1961 KiB  
Article
Artificial-Neural-Network-Driven Innovations in Time-Varying Process Diagnosis of Low-K Oxide Deposition
by Seunghwan Lee, Yonggyun Park, Pengzhan Liu, Muyoung Kim, Hyeong-U Kim and Taesung Kim
Sensors 2023, 23(19), 8226; https://doi.org/10.3390/s23198226 - 02 Oct 2023
Viewed by 934
Abstract
To address the challenges in real-time process diagnosis within the semiconductor manufacturing industry, this paper presents a novel machine learning approach for analyzing the time-varying 10th harmonics during the deposition of low-k oxide (SiOF) on a 600 Å undoped silicate glass thin liner [...] Read more.
To address the challenges in real-time process diagnosis within the semiconductor manufacturing industry, this paper presents a novel machine learning approach for analyzing the time-varying 10th harmonics during the deposition of low-k oxide (SiOF) on a 600 Å undoped silicate glass thin liner using a high-density plasma chemical vapor deposition system. The 10th harmonics, which are high-frequency components 10 times the fundamental frequency, are generated in the plasma sheath because of their nonlinear nature. An artificial neural network with a three-hidden-layer architecture was applied and optimized using k-fold cross-validation to analyze the harmonics generated in the plasma sheath during the deposition process. The model exhibited a binary cross-entropy loss of 0.1277 and achieved an accuracy of 0.9461. This approach enables the accurate prediction of process performance, resulting in significant cost reduction and enhancement of semiconductor manufacturing processes. This model has the potential to improve defect control and yield, thereby benefiting the semiconductor industry. Despite the limitations imposed by the limited dataset, the model demonstrated promising results, and further performance improvements are anticipated with the inclusion of additional data in future studies. Full article
(This article belongs to the Special Issue Recent Innovations in Plasma Sensing and Diagnosis Technology)
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13 pages, 1736 KiB  
Article
Diagnosing Time-Varying Harmonics in Low-k Oxide Thin Film (SiOF) Deposition by Using HDP CVD
by Yonggyun Park, Pengzhan Liu, Seunghwan Lee, Jinill Cho, Eric Joo, Hyeong-U Kim and Taesung Kim
Sensors 2023, 23(12), 5563; https://doi.org/10.3390/s23125563 - 14 Jun 2023
Viewed by 1355
Abstract
This study identified time-varying harmonic characteristics in a high-density plasma (HDP) chemical vapor deposition (CVD) chamber by depositing low-k oxide (SiOF). The characteristics of harmonics are caused by the nonlinear Lorentz force and the nonlinear nature of the sheath. In this study, a [...] Read more.
This study identified time-varying harmonic characteristics in a high-density plasma (HDP) chemical vapor deposition (CVD) chamber by depositing low-k oxide (SiOF). The characteristics of harmonics are caused by the nonlinear Lorentz force and the nonlinear nature of the sheath. In this study, a noninvasive directional coupler was used to collect harmonic power in the forward and reverse directions, which were low frequency (LF) and high bias radio frequency (RF). The intensity of the 2nd and 3rd harmonics responded to the LF power, pressure, and gas flow rate introduced for plasma generation. Meanwhile, the intensity of the 6th harmonic responded to the oxygen fraction in the transition step. The intensity of the 7th (forward) and 10th (in reverse) harmonic of the bias RF power depended on the underlying layers (silicon rich oxide (SRO) and undoped silicate glass (USG)) and the deposition of the SiOF layer. In particular, the 10th (reverse) harmonic of the bias RF power was identified using electrodynamics in a double capacitor model of the plasma sheath and the deposited dielectric material. The plasma-induced electronic charging effect on the deposited film resulted in the time-varying characteristic of the 10th harmonic (in reverse) of the bias RF power. The wafer-to-wafer consistency and stability of the time-varying characteristic were investigated. The findings of this study can be applied to in situ diagnosis of SiOF thin film deposition and optimization of the deposition process. Full article
(This article belongs to the Special Issue Recent Innovations in Plasma Sensing and Diagnosis Technology)
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