應用統計與資料採掘技術以提昇晶圓製造良率之研究
Other Title
The Application of Statistics and Data Mining Techniques to
Improving Semiconductor Manufacturing Yield
Improving Semiconductor Manufacturing Yield
Date Issued
2003
Date
2003
Author(s)
DOI
912416H002007
Abstract
Ramping up yield in semiconductor
manufacturing is getting more difficult as the
feature size of IC device continuously shrinks
down. How to efficiently detect and diagnose the
yield loss from the mass volume of manufacturing
data is becoming critical to enhance a
manufacturer’s competition capability. In this
research, we focus on techniques for
automatically extracting process knowledge from
production database for fault diagnosis and
optimizing device performance with fixed target.
An integrated parametric analysis scheme is
developed with supplemented graphical methods
to facilitate interpretation of results. It consists of five phases: Device Variation Partition, Key Node
Screening, Linear Equipment Modeling, Graph
Aided Interpretation, and Control Policy
Re-evaluation. The concepts of quality control,
data mining, and process knowledge are
integrated in this scheme. Field data case study
shows that the integrated parametric analysis
scheme is able to diagnose the parametric yield
problem, help engineers construct the knowledge
base, predict the yield, and provide insights for
yield enhancement.
Subjects
Yield Improvement
Statistical Model
Data Mining
Publisher
臺北市：國立臺灣大學工商管理學系
Type
report
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