Micro-range uniformity control of the etching profile in the OLED display mass production referring to the PI-VM model

Park, Seolhye; Seong, Jaegu; Noh, Yeongil; Park, Yoona; Jang, Yongsuk; Cho, Taeyoung; Yang, Jungjin; Kim, Gon−Ho

doi:10.1063/5.0048963

Cited by 7 publications

(21 citation statements)

References 40 publications

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“…The reader is referred to corresponding comprehensive surveys by Brunton and Vinuesa et al 191,192 Concerning data-driven process control and MPC, it should be noted that the plasma information based VM methodology for LTP etching processes previously introduced may be adapted based on physical or data-driven model data. 19,20,193 Despite a possible discrepancy between models and reality, in particular systematic deviations may be straightforwardly taken into account in a data-driven approach by means of discrepancy learning. A learnable translation layer may suffice to capture and combine the relevant information by means of data-fusion.…”

Section: Data-driven Discharge Surrogate Modelingmentioning

confidence: 99%

“…15 Plasma virtual metrology (VM) was conducted based on multivariate sensor data, 16 with regard to real-time fault detection in reactive ion etching, 17 batch process characterization in semiconductor fabrication, 18 and a deep learning VM framework based on OES data 19 and "plasma information" descriptors. 20 Further examples have devised an inverse reconstruction of intrinsic plasma properties, such as the electron energy distribution function from OES diagnostics data 21 as well as an active learning guided scheme based on Fourier transform infrared spectroscopy data for parameter space exploration. 22 In contrast to these previous examples, which rely on the experimental and diagnostics data, the focus of this paper will be on data-driven aspects of plasma modeling and simulation.…”

Section: Introductionmentioning

confidence: 99%

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Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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Machine learning has had an enormous impact in many scientific disciplines. It has also attracted significant interest in the field of low-temperature plasma (LTP) modeling and simulation in past years. Its application should be carefully assessed in general, but many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and datadriven modeling. In this survey, we approach two main objectives: (a) we review the state-of-the-art, focusing on approaches to LTP modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data.

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Section: Data-driven Discharge Surrogate Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Trieschmann,

Vialetto,

Gergs

2023

J. Micro/Nanopattern. Mats. Metro.

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“…They have been used to predict various process performances such as etch rate, deposition rate, defect particles, etching profile, deposited thin film quality, and spatial uniformity of the processed results. They have been applied to the control and management of the OLED mass production lines last six years [577][578][579][580][581][582][583].…”

Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 99%

“…Developed PI-VM algorithms were applied to the mass production line of the OLED display manufacturing to solve four kinds of problems that occurred in the real field: The defect particle caused process fault prediction [578], root cause analysis of the high-aspect-ratio contact (HARC) etching process faults [579], the management of the mass production discontinuities with a proper application of the in-situ dry cleaning (ISD) [580], and the micro-uniformity problems in the process results [581]. These PI-VM models optimized for each issue have shown enough prediction accuracy to apply for the long-periodic mass production running.…”

Section: B Data-driven Approaches For Plasma-assistedmentioning

confidence: 99%

“…Several data-driven approaches have taken PSI into account macroscopically for plasma process control. They used plasma information based virtual metrology for plasma etching with experimental data sources [600], as well as model predictive control for atmospheric pressure plasma dose delivery [601] or reactive magnetron sputtering close to mode transition [602]. In contrast, theoretical multi-scale analyses of technological plasmas have been restricted to classical modeling and simulation (e.g., combining molecular dynamics, binary collision approximation, and kinetic Monte Carlo models at the atomic level [603]; or unidirectional coupling the reactor scale to the feature scale in complex capacitive radio frequency plasmas [604]; list not exhaustive).…”

Section: Data Management In Manufacturingmentioning

confidence: 99%

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2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

Self Cite

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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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Observation of the floating sheath distribution on Al2O3 and silicon targets adjacent to a DC biased metal substrate

Bae,

Kim,

Lee

et al. 2024

Current Applied Physics

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Micro-range uniformity control of the etching profile in the OLED display mass production referring to the PI-VM model

Cited by 7 publications

References 40 publications

Review: Machine learning for advancing low-temperature plasma modeling and simulation

Review: Machine learning for advancing low-temperature plasma modeling and simulation

2022 Review of Data-Driven Plasma Science

Observation of the floating sheath distribution on Al2O3 and silicon targets adjacent to a DC biased metal substrate

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