Application of Data Mining for Improving Yield in Wafer Fabrication System

Lecture notes in computer sciencePublished 1 January 2005

Donghyun Baek, In‐Jae Jeong, Chang Hee Han

DOI10.1007/11424925_25

Source URL

Citations8

SJR quartileQ2

SJR score0.35

SNIP0.55

TL;DR

An information system, Y2R-PLUS (Yield Rapid Ramp-up, Prediction, analysis & Up Support) that is developed in order to analyze wafer yield in a Korea semiconductor manufacturer is demonstrated.

Abstract

This paper presents a comprehensive and successful application of data mining methodologies to improve wafer yield in a semiconductor wafer fabrication system. To begin with, this paper applies a clustering method to automatically identify AUF (Area Uniform Failure) phenomenon from data instead of visual inspection that bad chips occurs in a specific area of wafer. Next, sequential pattern analysis and classification methods are applied to find out machines and parameters that are cause of low yield, respectively. Finally, this paper demonstrates an information system, Y2R-PLUS (Yield Rapid Ramp-up, Prediction, analysis & Up Support) that is developed in order to analyze wafer yield in a Korea semiconductor manufacturer.

Keywords

Computer ScienceEngineering

MIT Press eBooksKnowledge Discovery in Databases

1,758 Citations1991Gregory Piateski, William Frawley

IEEE Transactions on Semiconductor ManufacturingData mining for improving a cleaning process in the semiconductor industry

107 Citations2002Dan Braha, Armin Shmilovici

The paper suggests that data mining methodologies may be particularly useful when data is scarce, and the various physical and chemical parameters that affect the process exhibit highly complex interactions.

IEEE Transactions on Robotics and AutomationData mining of printed-circuit board defects

87 Citations2001Andrew Kusiak, C. Kurasek

An industrial case study in which data mining has been applied to solve a quality engineering problem in electronics assembly using a data mining approach to identify causes of quality faults in an industrial application is discussed.

Journal of Materials Processing TechnologyApplication of data mining and process knowledge discovery in sheet metal assembly dimensional variation diagnosis

31 Citations2002Jun Lian, Xuexian Lai +2 more

A dimensional variation diagnostic reasoning and decision approach through the combination of data mining (DM) and knowledge discovery techniques to fulfill the “hypothesis and validation” characterized variation causes reasoning procedure.

Computers & Chemical EngineeringSupervised learning for the analysis of process operational data

13 Citations2000Yoshiyuki Yamashita

Decision tree inducer can directly extract reasonable operational rules from the data-set with no previous knowledge and by integrating the feature-subset selection wrapper algorithm, Naive-Bayes classifier and nearest-neighbor classifier can also estimate the action of operation successfully.

Quality EngineeringUse of Ridge Regression in the Improved Control of Casting Processes

4 Citations1996Walton M. Hancock, Jae-Wook Yoon +1 more

Journal of Korean Institute of Industrial EngineersAdaptive Decision Tree Algorithm for Data Mining in Real-Time Machine Status Database

2 Citations2000Jun‐Geol Baek, Kangho Kim +2 more

This article presents an adaptive decision tree algorithm for dynamically reasoning machine failure cause out of real-time, large-scale machine status database and confirms that this model outperforms previously proposed decision tree models.