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如何借助机器学习和专家系统来推动工艺质量的提高

了解这项技术如何成为在制造业中打造“会思考的机器”的基石
Drive Process Quality using machine earning and expert Systems
您是否曾想过,杂货店会如何理货? 比如,杂货店是如何知道要把黄油放在面包旁边的? Netflix 是如何根据观看历史推荐电影的?
这些就是基于数据提取技术的“购物篮分析”示例,这项技术也叫做频繁模式挖掘。 这种技术会寻找重复出现的关系,从而在不同数据项之间找到关联。 当我们将这项技术运用到半导体制造时,它帮助我们深入了解工艺以实现快速的质量提升。 此类算法完全能够在尽可能减少人为干预的情况下处理海量数据,从而获得最优的解决方案。 应用材料公司的团队采用了机器学习的研究,这些研究成果帮助我们识别生产设备和工艺流程的行为,从而进行产线的定制化、持续和自动化的调整。

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关于作者

Vishali Ragam, Global Product Manager, SPC
Vishali Ragam, Global Product Manager, SPC
Vishali has been working in the semiconductor industry for more than 15 years. Prior to joining Applied Materials, she worked at Micron Technology, first as a process engineer and then as a senior quality engineer. She has been with Applied for seven years, having joined the company as a quality solutions architect. Vishali is currently a Global Product Manager overseeing SmartFactory SPC3D, an advanced process control (APC) engine that runs statistics to determine if processes are within spec to improve product yield. Vishali has an MS in mechanical engineering from Oklahoma State University, and a bachelor’s in mechanical engineering from Osmania University, in Hyderabad, Telangana, India.