Paper Info
Title
Insight extraction for semiconductor manufacturing processes
Abstract
In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.
Year
DOI
Venue
2014
10.1109/CoASE.2014.6899415
Automation Science and Engineering
Keywords
Field
DocType
design of experiments,learning (artificial intelligence),production engineering computing,production equipment,semiconductor industry,design of experiments,information extraction,learning-by-observation phase,machine learning techniques,metal deposition process,process analysis,process engineer,production equipment,production processes,semiconductor manufacturing environment,semiconductor manufacturing processes,Metal Deposition,Moving Window,Semiconductor Manufacturing,Sparse Regression,Virtual Metrology
Process engineering,Semiconductor device modeling,Metrology,Semiconductor device fabrication,Computer-integrated manufacturing,Manufacturing engineering,Process analysis,Engineering,Metal deposition,Design of experiments
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
6
Name
Order
Citations
PageRank
Simone Pampuri18410.20
Gian Antonio Susto210622.76
Jian Wan322.12
Adrian B. Johnston401.01
Paul G. O'Hara500.68
Seán F. McLoone622424.90