利用基于注意力的长短期记忆机器学习改进等离子体蚀刻终点检测

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ye Jin Kim, Jung Ho Song, Ki Hwan Cho, Jong Hyeon Shin, Jong Sik Kim, Jung Sik Yoon, Sang Jeen Hong
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引用次数: 0

摘要

现有的蚀刻端点检测(EPD)方法主要基于单一波长,存在信噪比低、无法考虑时间序列数据的长期依赖性等局限性。为解决这些问题,本研究提出了一种使用长短期记忆(LSTM)(一种递归神经网络(RNN))的时间序列数据背景。所提出的方法基于在二氧化硅蚀刻过程中通过光学发射光谱(OES)数据收集到的时间序列数据。在对 LSTM 模型进行训练后,与现有方法相比,所提出的方法通过考虑整个时间序列,能够更准确地检测出蚀刻终点。在给定条件下,LSTM 模型的准确率达到了 97.1%,这表明考虑时间序列数据的流程和上下文可以显著降低误检率。为了提高所提出的 LSTM 模型的性能,我们创建了一个基于注意力的 LSTM 模型,并证实该模型的准确率为 98.2%,与现有的 LSTM 模型相比性能有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Memory Machine Learning
Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data using 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 emission spectroscopy (OES) data during the SiO2 etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endpoint more accurately than existing methods by considering the entire time series. The LSTM model achieved an accuracy of 97.1% in a given condition, which shows that considering the flow and context of time series data can significantly reduce the false detection rate. To improve the performance of the proposed LSTM model, we created an attention-based LSTM model and confirmed that the model accuracy is 98.2%, and the performance is improved compared to that of the existing LSTM model.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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