Machine Learning Prediction of Electron Density and Temperature from Optical Emission Spectroscopy in Nitrogen Plasma

Park, Jun-Hyoung; Cho, Ji-Ho; Yoon, Jung‐Sik; Song, Jeongmin

doi:10.3390/coatings11101221

Cited by 6 publications

(4 citation statements)

References 21 publications

(27 reference statements)

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“…Furthermore, machine learning is continuously gaining importance and gradually establishing itself as a control tool in the processing and analysis of optical emission spectra. Park et al [2] employed machine learning to predict plasma parameters, specifically the electron density and electron temperature, from optical emission spectroscopy (OES) data. They propose the use of this method for the noninvasive determination of relevant plasma parameters.…”

Section: Introductionmentioning

confidence: 99%

About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction

Haase,

Sayyed,

Langer

et al. 2024

Plasma

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In this study, we present the Method of Spectral Redundancy Reduction (MSRR) for analyzing OES (optical emission spectroscopy) data of dry etching processes based on the principles of spectral clustering. To achieve this, the OES data are transformed into abstract graph matrices whose associated eigenvectors directly indicate anomalies in the data set. We developed an approach that allows for the reduction in temporally resolved optical emission spectra from plasma structuring processes in such a way that individual emission lines can be algorithmically detected, which exhibit a temporal behavior different from the collective behavior of the temporally resolved overall spectrum. The proportion of emission lines that behave consistently throughout the entire process duration is not considered. Our work may find applications in which OES is used as a process-monitoring technique, especially for low-pressure plasma processing. The major benefit of the developed method is that the scale of the original data is kept, making physical interpretations possible despite data reductions.

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Section: Introductionmentioning

confidence: 99%

About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction

Haase,

Sayyed,

Langer

et al. 2024

Plasma

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“…Alternatively, in the absence of theoretical models, ML has shown great promise for learning multivariable and nonlinear data-driven models from measurements [23][24][25]. Furthermore, ML can facilitate the development of so-called 'soft sensors' (aka virtual metrology [26,27]) that enable real-time diagnostics of plasma and surface properties using accessible and information-rich process measurements [28][29][30][31]. Real-time diagnostics of plasma and surface properties in turn facilitates feedback control of LTP processes, another area where ML can play an important role towards realizing desired LTP processing outcomes on complex interfaces [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Foundations of machine learning for low-temperature plasmas: methods and case studies

Bonzanini¹,

Shao²,

Graves³

et al. 2023

Plasma Sources Sci. Technol.

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Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

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“…Semiconductor manufacturing industries are very much interested in process diagnoses in terms of equipment status variable identification, in situ optical plasma monitoring, and RF voltage/current monitoring [5][6][7] data. Semiconductor manufacturing processes consist of hundreds of consecutive steps of various thin-film depositions and their selective removal, which are performed with predetermined equipment operation conditions called process recipes.…”

Section: Introductionmentioning

confidence: 99%

Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment

Kwon

Hong

2022

Electronics

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To minimize wafer yield losses by misprocessing during semiconductor manufacturing, faster and more accurate fault detection during the plasma process are desired to increase production yields. Process faults can be caused by abnormal equipment conditions, and the performance drifts of the parts or components of complicated semiconductor fabrication equipment are some of the most unnoticed factors that eventually change the plasma conditions. In this work, we propose improved stability and accuracy of process fault detection using optical emission spectroscopy (OES) data. Under a controlled experimental setup of arbitrarily induced fault scenarios, the extended isolation forest (EIF) approach was used to detect anomalies in OES data compared with the conventional isolation forest method in terms of accuracy and speed. We also used the OES data to generate features related to electron temperature and found that using the electron temperature features together with equipment status variable identification data (SVID) and OES data improved the prediction accuracy of process/equipment fault detection by a maximum of 0.84%.

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Machine Learning Prediction of Electron Density and Temperature from Optical Emission Spectroscopy in Nitrogen Plasma

Cited by 6 publications

References 21 publications

About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction

About the Data Analysis of Optical Emission Spectra of Reactive Ion Etching Processes—The Method of Spectral Redundancy Reduction

Foundations of machine learning for low-temperature plasmas: methods and case studies

Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment

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