Development of Virtual Metrology Using Plasma Information Variables to Predict Si Etch Profile Processed by SF6/O2/Ar Capacitively Coupled Plasma

Kwon, Ji-Won; Ryu, Sangwon; Park, Jihoon; Lee, Haneul; Jang, Yongwon; Park, Seolhye; Kim, Gon−Ho

doi:10.3390/ma14113005

Cited by 19 publications

(13 citation statements)

References 20 publications

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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%

“…Plasma diagnostics paired with appropriate sensor technologies can reduce the advanced data processing load while maintaining or even improving model accuracy. This is done through direct extraction of variables that should correlate with target metrics via theory or model [591][592][593][594], a methodology termed "data quality improvement." Data quality improvement relies heavily on the selection of appropriate in-situ sensors, which in turn requires specific plasma domain knowledge:…”

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

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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: 99%

Section: Data Management In Manufacturingmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is commonly used for detecting the etching process endpoint and has been actively investigated for fault detection of plasma equipment using statistics-based modeling approaches [ 12 , 13 ]. A high-performance model can be implemented by manipulating OES data to obtain plasma information, including electron temperature and electron density [ 14 , 15 ], or by selecting radical peaks related to the process based on domain knowledge [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data

Choi

Seol

Kim

et al. 2023

Sensors

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This research proposes an application of generative adversarial networks (GANs) to solve the class imbalance problem in the fault detection and classification study of a plasma etching process. Small changes in the equipment part condition of the plasma equipment may cause an equipment fault, resulting in a process anomaly. Thus, fault detection in the semiconductor process is essential for success in advanced process control. Two datasets that assume faults of the mass flow controller (MFC) in equipment components were acquired using optical emission spectroscopy (OES) in the plasma etching process of a silicon trench: The abnormal process changed by the MFC is assumed to be faults, and the minority class of Case 1 is the normal class, and that of Case 2 is the abnormal class. In each case, additional minority class data were generated using GANs to compensate for the degradation of model training due to class-imbalanced data. Comparisons of five existing fault detection algorithms with the augmented datasets showed improved modeling performances. Generating a dataset for the minority group using GANs is beneficial for class imbalance problems of OES datasets in fault detection for the semiconductor plasma equipment.

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“…Arshad et al proposed using a VI probe and optical monitoring sensor to detect abnormal plasma discharge in a plasma-assisted deposition process [10]. Recent research on virtual metrology using plasma information variables derived from OES data has been presented to predict plasma etch results [11].…”

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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Development of Virtual Metrology Using Plasma Information Variables to Predict Si Etch Profile Processed by SF6/O2/Ar Capacitively Coupled Plasma

Cited by 19 publications

References 20 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data

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

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