Performance Evaluation of CNN-Based End-Point Detection Using In-Situ Plasma Etching Data

Kim, Bobae; Im, Sungbin; Yoo, Geonwook

doi:10.3390/electronics10010049

Cited by 18 publications

(11 citation statements)

References 47 publications

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“…Despite the requirement of highly complex quadratic programming, the Support Vector Machine (SVM) is increasingly used for solving classification and regression problems in recent times [44,47]. SVM tries to form different data clusters by constructing hyperplanes in high dimensional space in order to distinguish a different class of data while dealing with classification problems.…”

Section: Support Vector Machinementioning

confidence: 99%

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

2021

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Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

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Section: Support Vector Machinementioning

confidence: 99%

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

2021

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“…APC enables real-time process monitoring and control using sensors to instantly identify and control the critical process shifts [21]. Sensor-based APC studies such as a study that proposed strict control of CD and phase angle in a photomask dry-etch process using the RF sensor and a study using an in situ plasma monitoring sensor to process diagnosis and endpoint detection in etch process were conducted [22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

et al. 2021

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Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

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“…The PCA method can simplify the complex correlations between several measured lines and the resulting plasma parameters by reducing the dimensions of the datasets. Thus, PCA has been suggested as a method for analyzing large amounts of OES data [14,22]. PCA transforms the input variables to a set of orthogonal variables known as principal components (PCs), which are linear combinations of the original variables.…”

Section: Principal Component Analysis (Pca) For Oes Data Vs Plasma Parametersmentioning

confidence: 99%

“…Choi et al [11] developed a machine learning-based virtual metrology (VM) model on film thickness in amorphous carbon layer deposition process using in situ OES sensor data. Additionally, Yue et al [12], Han et al [13], and Kim et al [14] proposed principal component analysis (PCA) of OES spectrum data for endpoint detection of plasma etching processes in the semiconductor industry. Even though OES has the advantage of non-invasiveness, it provides a huge amount of information.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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We present a non-invasive approach for monitoring plasma parameters such as the electron temperature and density inside a radio-frequency (RF) plasma nitridation device using optical emission spectroscopy (OES) in conjunction with multivariate data analysis. Instead of relying on a theoretical model of the plasma emission to extract plasma parameters from the OES, an empirical correlation was established on the basis of simultaneous OES and other diagnostics. Additionally, we developed a machine learning (ML)-based virtual metrology model for real-time Te and ne monitoring in plasma nitridation processes using an in situ OES sensor. The results showed that the prediction accuracy of electron density was 97% and that of electron temperature was 90%. This method is especially useful in plasma processing because it provides in-situ and real-time analysis without disturbing the plasma or interfering with the process.

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Performance Evaluation of CNN-Based End-Point Detection Using In-Situ Plasma Etching Data

Cited by 18 publications

References 47 publications

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment

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

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