A tutorial on support vector regression
Statistics and Computing
Implementing a data mining solution for enhancing carpet manufacturing productivity
Knowledge-Based Systems
Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
Data based segmentation and summarization for sensor data in semiconductor manufacturing
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Nowadays, the semiconductor manufacturing becomes very complex, consisting of hundreds of individual processes. If a faulty wafer is produced in an early stage but detected at the last moment, unnecessary resource consumption is unavoidable. Measuring every wafer's quality after each process can save resources, but it is unrealistic and impractical because additional measuring processes put in between each pair of contiguous processes significantly increase the total production time. Metrology, as is employed for product quality monitoring tool today, covers only a small fraction of sampled wafers. Virtual metrology (VM), on the other hand, enables to predict every wafer's metrology measurements based on production equipment data and preceding metrology results. A well established VM system, therefore, can help improve product quality and reduce production cost and cycle time. In this paper, we develop a VM system for an etching process in semiconductor manufacturing based on various data mining techniques. The experimental results show that our VM system can not only predict the metrology measurement accurately, but also detect possible faulty wafers with a reasonable confidence.