首页|Research from University of Texas Dallas Reveals New Findings on Machine Learnin g (Case study in machine learning for predicting moderate pressure plasma behavi or)
Research from University of Texas Dallas Reveals New Findings on Machine Learnin g (Case study in machine learning for predicting moderate pressure plasma behavi or)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news originating from the University of Texas Dallas by NewsRx correspondents, research stated, “Modeling and forecasti ng the dynamics of complex systems, such as moderate pressure capacitively coupl ed plasma (CCP) systems, remains a challenge due to the interactions of physical and chemical processes across multiple scales.” Funders for this research include Applied Materials. Our news editors obtained a quote from the research from University of Texas Dal las: “Historically, optimization for a given application would be accomplished v ia a design of experiment (DOE) study across the various external control parame ters. Machine learning (ML) techniques show the potential to “forecast” process conditions not tested in a traditional DOE study and thereby allow better optimi zation and control of a plasma tool. In this article, we have used standard DOE as well as ML predictions to analyze I-V data in a moderate-pressure CCP system. We have demonstrated that supervised regression ML techniques can be a useful t ool for extrapolating data even when a plasma system is undergoing a transition in the heating mode, in this case from the alpha to gamma mode.”
University of Texas DallasCyborgsEme rging TechnologiesMachine Learning
NETL
NSTL
