首页|Recent Findings from Myongji University Highlight Research in Machine Learning ( Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Me mory Machine Learning)
Recent Findings from Myongji University Highlight Research in Machine Learning ( Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Me mory Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news originating from Yongin, South Korea, by Ne wsRx correspondents, research stated, “Existing etch endpoint detection (EPD) me thods, primarily based on single wavelengths, have limitations, such as low sign al-to-noise ratios and the inability to consider the long-term dependencies of t ime series data.” Financial supporters for this research include National Research Council of Scie nce & Technology; Plasma E.I. Conversion Research Center. The news journalists obtained a quote from the research from Myongji University: “To address these issues, this study proposes a context of time series data usi ng long short-term memory (LSTM), a kind of recurrent neural network (RNN). The proposed method is based on the time series data collected through optical emiss ion spectroscopy (OES) data during the SiO2 etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endp oint more accurately than existing methods by considering the entire time series .”
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