Characteristics of a non-Maxwellian electron energy distribution in a low-pressure argon plasma

Park, Seolhye; Choe, Jae-Myung; Roh, Hyun-Joon; Kim, Gon−Ho

doi:10.3938/jkps.64.1819

Cited by 18 publications

(6 citation statements)

References 24 publications

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“…79 equipment engineering system (EES) sensing variables from the power, pressure, gas, chiller, heater, and exhaust system and 1670 parameters from the optical emission spectroscopy (OES) intensities were combined into the PCs and were regressed . The distribution shape varies from the well-known high-energy tail developed Maxwellian distribution with b=1 to the curtailed Druyvesteyn distribution with b=2 in general [584,585]. Finally, by adopting pre-sheath potential and surface passivation representing PI parameters synthesized from the monitored OES and EES data, the prediction performance of the VM was enhanced to R 2 =96.9%, as shown in Fig.…”

Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 94%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 94%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

Self Cite

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“…at the sheath edge, the sheath edge potential V s from equation ( 4), can be obtained [10,[19][20][21]…”

Section: Pi-vm Modeling For Root Cause Analysis Of the Process Faultmentioning

confidence: 99%

“…This shape factor b is very sensitive to the microscopic variation of the process plasmas. Thus, we considered generalized non-Maxwellian EEDFs [8,21,22]. The coefficients c 1 and c 2 are determined by the normalization of EEDF and the definition of the averaged effective electron temperature as:…”

Section: Pi-vm Modeling For Root Cause Analysis Of the Process Faultmentioning

confidence: 99%

“…where V p is the plasma potential with respect to the potential at the sheath-presheath edge, one of the most important plasma parameters in general. Although the absolute diagnosis of the V p by using the OES sensor data installed for EPD was impossible, we could track the variation of V p during the plasma process with the application of the measured EEDF shape factor and plasma temperature [21]. Electronegativity α at the sheath edge was adopted from Thorsteinsson and Gudmundsson's results according to the process condition [23].…”

Section: Pi-vm Modeling For Root Cause Analysis Of the Process Faultmentioning

confidence: 99%

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Data-driven plasma science based plasma etching process design in OLED mass production referring to PI-VM

Park,

Seong,

Park

et al. 2024

Plasma Phys. Control. Fusion

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The production efficiencies of OLED (Organic Light Emitting Diode) displays and semiconductor manufacturing have been dramatically improving with the help of plasma physics and engineering technology by utilizing a process monitoring methodology based on physical domain knowledge. This domain knowledge consists of plasma-heating and sheath physics, plasma chemistry, and plasma-material surface reaction kinetics. They were applied to the PI-VM (Plasma Information based Virtual Metrology) algorithm with the plasma diagnostics and noticeably enhanced process prediction performance by parameterizing plasma information (PI) in various processes of OLED display and semiconductor manufacturing fabs. PI-VM has shown superior process prediction accuracy, which can trace the states of processing plasmas as an application of data-driven plasma science compared to the classical statistics and machine learning-based virtual metrologies; thus, various plasma processes have been managed and controlled with the help of the PI-VM models. More than this, we have adopted the PI-VM model to optimize the patterning architecture and plasma processes simultaneously. The best combination of the etching pattern structure and plasma condition was adjustable based on the detailed understanding of the angular distribution of sputtered atoms from the etching target surface and their interaction with the plasma sheath based on the PI-VM modeling for etching profile failure prediction.

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“…By the application of the corona equilibrium (CE) model for metal etching plasma operated in 10 mTorr, plasma characteristics parameters (T e,eff , b) which denotes that the thermal equilibrium property can be measured by using Ar line intensities of OES data with the described model in [12]. Figure 1 compares the observed plasma parameters monitored during the metal etching process for normal and fault glasses processed in one chamber.…”

Section: Defect Generation Mechanism Analysis Based On the Oes Data A...mentioning

confidence: 99%

Application of PI-VM for management of the metal target plasma etching processes in OLED display manufacturing

Park

Cho

Jang

et al. 2018

Plasma Phys. Control. Fusion

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Generation of the defect particles during the plasma-assisted metal dry etching process is induced by the various mechanisms. Most of these mechanisms are caused by the non-volatility of metalhalide compounds generated during the etching process. Degeneration of the metal etching process chamber condition is observed as the frequent process fault caused by the defects, but the worse condition is not recovered by itself. Because of this property of the metal etching process, proper work of the preventive maintenance (PM) to restore the process chamber or the addition of a discharge cleaning step is required periodically. However, inadequate PM or discharge cleaning by the uncertain cause analysis of the defect generation should be a just temporary remedy, and might lead the repetition of similar problems. To solve this problem, the virtual metrology (VM) model based on the plasma information (PI) parameters, known as PI-VM, was developed and applied for the defect control of the metal layer dry etching processes in organic light emitting diode (OLED) display manufacturing. To obtain the information about the generation rates of non-volatile compounds and their removal rates by the exhaustion system, PI parameters are designed with the consideration of the reaction kinetics in the metal etching plasma volume, sheath, and reacting surface using the big data of equipment engineering system (EES) and optical emission spectroscopy (OES) accumulated during the mass production process. The developed PI-VM index could be applied to a 2-3 h earlier alarm system for the defect occurrence, and had succeeded over 90% of alarm rate. This PI-VM alarm was applied to predictive control of the process by the early substitution of the discharge cleaning step or by the repair of the proper parts in the process chamber. By the control of processes based on the predicted results of the PI-VM, management of the mass production line with about 30% decreased defect was possible in OLED display manufacturing.

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Characteristics of a non-Maxwellian electron energy distribution in a low-pressure argon plasma

Cited by 18 publications

References 24 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

Data-driven plasma science based plasma etching process design in OLED mass production referring to PI-VM

Application of PI-VM for management of the metal target plasma etching processes in OLED display manufacturing

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