Wavelet-coupled backpropagation neural network as a chamber leak detector of plasma processing equipment

Kim, Byungwhan; Kwon, Sanghee

doi:10.1016/j.eswa.2010.11.088

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…142 Due to the emergence of deep learning in 2006, significant progress has been made in the feedforward neural network (FFNN), convolutional neural network (CNN), and recurrent neural network (RNN). 143,144 The deep convolutional neural network (DCNN) has drawn great attention since 2012. 145 It contains convolutional layers and pooling layers for feature extraction.…”

Section: Applicationmentioning

confidence: 99%

Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

Jiao

et al. 2020

ACS Chem. Health Saf.

126

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Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions and decisionmaking. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this Review, commonly used ML/DL tools and concepts as well as popular ML/DL algorithms are introduced and discussed. More than 100 papers have been categorized and summarized to present the current development of ML/DL-based research in the area of chemical health and safety. In addition, the limitation of current studies and prospects of ML/DL-based study are also discussed. This Review can serve as useful guidance for researchers who are interested in implementing ML/DL into chemical health and safety research and for readers who try to learn more information about novel ML/DL techniques and applications.

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Section: Applicationmentioning

confidence: 99%

Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

Jiao

et al. 2020

ACS Chem. Health Saf.

126

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“…This study proposed neural networks as the research method to analyze the semiconductor machine outliers. Neural network analysis has been validated to show the capability of analyzing the plasma processing equipment [25], reactive ion etching [26], plasma etch process [27], chamber leak detector of plasma processing equipment [28], and so forth.…”

Section: 4mentioning

confidence: 99%

Applying Artificial Neural Network to Predict Semiconductor Machine Outliers

Yang

Chao

et al. 2013

Mathematical Problems in Engineering

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Advanced semiconductor processes are produced by very sophisticated and complex machines. The demand of higher precision for the monitoring system is becoming more vital when the devices are shrunk into smaller sizes. The high quality and high solution checking mechanism must rely on the advanced information systems, such as fault detection and classification (FDC). FDC can timely detect the deviations of the machine parameters when the parameters deviate from the original value and exceed the range of the specification. This study adopts backpropagation neural network model and gray relational analysis as tools to analyze the data. This study uses FDC data to detect the semiconductor machine outliers. Data collected for network training are in three different intervals: 6-month period, 3-month period, and one-month period. The results demonstrate that 3-month period has the best result. However, 6-month period has the worst result. The findings indicate that machine deteriorates quickly after continuous use for 6 months. The equipment engineers and managers can take care of this phenomenon and make the production yield better.

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“…24 In the present study where training times were not an issue and the network is of high complexity, the most appropriate for this application was identified as the backpropagation algorithm, using the training approach described by 19 from earlier work by. 23 Backpropagation neural network training has been shown to be extremely useful for identification and characterisation of many kinds of sources based on their spectral signatures, including stars, 25 plasma chamber leaks 26 and EEGs used to detect sleep patterns in humans. 27 In comparison to some other neural network training methods, backpropagation is relatively easy to implement.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Use of artificial neural networks in measuring characteristics of shielded plutonium for arms control

Owen

Chambers

2012

J. Anal. At. Spectrom.

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Artificial neutral networks were developed for use as a potential 'information barrier' technology in the verification of arms control treaty accountable items. They were used to identify and measure specific attributes from g-ray spectra. These attributes included the presence or absence of plutonium, the plutonium Pu-239/Pu-240 isotopic ratio or 239 Pu content and the material age. A set of over 400 training spectra were generated using a spectral simulation software package and various methods for the selection of input data were tested. An input data set which discounted low energy regions susceptible to shielding effects was found to be most effective. Once trained, the network correctly identified the presence or absence of plutonium from real g-ray spectra. Accurate results were also achieved for estimating the content of 239 Pu. In simulated test spectra a root mean squared error (RMSE) of less than 0.1 was found when using the optimum number of inputs. The network was also able to distinguish between spectra from plutonium samples of different ages. Further work is planned to investigate the estimation of a confidence level for whether a specific threshold of 239 Pu content is exceeded. An improved training set is anticipated to improve accuracy in determining the material age, which was not achieved accurately.

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Wavelet-coupled backpropagation neural network as a chamber leak detector of plasma processing equipment

Cited by 5 publications

References 9 publications

Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

Applying Artificial Neural Network to Predict Semiconductor Machine Outliers

Use of artificial neural networks in measuring characteristics of shielded plutonium for arms control

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