摘要
机器人与机器学习每日新闻的一位新闻记者兼新闻编辑发表了关于人工智能的新研究结果。根据NewsRx记者来自德克萨斯大学达拉斯分校的新闻，研究表明，"由于物理和化学过程在多个尺度上的相互作用，对复杂系统的动力学建模和预测，如中压电容耦合等离子体(CCP)系统，仍然是一个挑战。"这项研究的资助者包括应用材料。我们的新闻编辑从德克萨斯大学Dal Las的研究中获得了一句话：“从历史上看，对给定应用的优化将通过实验(DOE)的设计在各种外部控制参数中完成。机器学习(ML)技术显示了“预测”传统DOE研究中未测试的工艺条件的潜力，从而允许更好地优化和控制等离子工具。在本文中，我们使用标准DOE和ML预测来分析中压CCP系统中的i-v数据。我们已经证明，监督回归ML技术可以成为推断数据的有用工具，即使等离子体系统正在经历加热模式的转变，在这种情况下，从阿尔法模式到伽马模式。
Abstract
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.”
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