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大数据挖掘在IC制造中的应用—缺陷预测模型_康盛

演讲嘉宾:康盛 , 中芯国际集成电路制造有限公司,质量与可靠性中心资深经理

Speaker: Sheng Kang, Senior Manager of the Quality and Reliability Center, SMIC

 

演讲主题:大数据挖掘在IC制造中的应用—缺陷预测模型
Topic: Big Data Mining in IC Manufacturing: Defect Prediction Model
 
摘要
2016年AlphaGo击败一个又一个人类的顶尖围棋高手,2017年AlphaGo Zero又击败了它的所有前辈,人类进入AI元年。随着信息技术的不断发展,数据的应用,特别是大数据在工业界的应用,对制造业也起着越来越重要的作用。IC制造作为高科技密集型的产业,其产品的尺寸和精密度就注定了它的生产离不开数据。本次演讲主要介绍了如何利用IC生产中的实时设备监控数据,在未知物理模型的情况下,运用数据挖掘和机器学习的方法,寻找产品缺陷的问题原因,并达到提升质量管控,优化产能,提高生产率的目的。让我们看到了AI在工业界的前景。通过一些实际案例分享,康先生将会介绍JMP的模型预测功能在实际IC制造过程中,对于产品缺陷预测和电性参数的趋势预测所发挥的作用。希望基于这些案例,能对其他领域的用户有所启发。
 
Abstract:
In 2016, AlphaGo defeated the top Go players one by one. And in 2017, AlphaGo Zero beat all its predecessors through self-learning without human experience. Since then, humans entered the first year of AI. With the continuous development of information technology, the application of data, especially the application of big data in industry, plays an increasingly important role in the manufacturing industry. Integrated circuit (IC) manufacturing is a high-tech-intensive industry. The products’ very small dimensions and high-precision requirements are doomed to have a very close relationship with data analysis. This talk mainly introduces how to use the real-time equipment monitoring data in IC production to find the root cause of defects with data mining and machine learning methods to achieve quality improvement, cost reduction and productivity enhancement. Let’s see the powers and prospects of AI in the industry. We’ll share some actual cases to introduce how to use the model predictive function of JMP in defect prediction and virtual metrology of WAT based on IC manufacturing data. We hope that these cases will inspire.