---
abstract: |-
We have analyzed manufacturing data from several different semiconductor
manufacturing plants, using decision tree induction software called
Q-YIELD. The software generates rules for predicting when a given product
should be rejected. The rules are intended to help the process engineers
improve the yield of the product, by helping them to discover the causes
of rejection. Experience with Q-YIELD has taught us the importance of
data engineering -- preprocessing the data to enable or facilitate
decision tree induction. This paper discusses some of the data engineering
problems we have encountered with semiconductor manufacturing data.
The paper deals with two broad classes of problems: engineering the features
in a feature vector representation and engineering the definition of the
target concept (the classes). Manufacturing process data present special
problems for feature engineering, since the data have multiple levels of
granularity (detail, resolution). Engineering the target concept is important,
due to our focus on understanding the past, as opposed to the more common
focus in machine learning on predicting the future.
altloc: []
chapter: ~
commentary: ~
commref: ~
confdates: ~
conference: IJCAI-95 Workshop on Data Engineering for Inductive Learning
confloc: 'Montreal, Quebec'
contact_email: ~
creators_id:
- 2175
creators_name:
- family: Turney
given: Peter
honourific: ''
lineage: ''
date: 1995
date_type: published
datestamp: 2003-04-16
department: ~
dir: disk0/00/00/28/91
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editors_id: []
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eprint_status: archive
eprintid: 2891
fileinfo: /style/images/fileicons/application_pdf.png;/2891/1/NRC%2D39163.pdf
full_text_status: public
importid: ~
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isbn: ~
ispublished: pub
issn: ~
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item_issues_count: 0
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item_issues_resolved_by: []
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item_issues_type: []
keywords: ~
lastmod: 2011-03-11 08:55:15
latitude: ~
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metadata_visibility: show
note: ~
number: ~
pagerange: 50-59
pubdom: FALSE
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refereed: TRUE
referencetext: |
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and
regression trees. California: Wadsworth.
Famili, A. and Turney, P.D. (1991), Intelligently helping the human planner in
industrial process planning, Artificial Intelligence for Engineering Design,
Analysis, and Manufacturing, Vol. 5, No. 2, pp. 109-124.
Famili, A. and Turney, P.D. (1992), Application of machine learning to industrial
planning and decision making, in Artificial Intelligence Applications in Manufacturing,
edited by A. Famili, S. Kim, and D. Nau, MIT Press, Cambridge,
MA, pp. 1-16.
Lavrac, N., & Dzeroski, S. (1994). Inductive Logic Programming: Techniques and
Applications. New York: Ellis Horwood.
Van Zant, P. (1986). Microchip Fabrication: A Practical Guide to Semiconductor
Processing. California: Semiconductor Services.
relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:47:22
subjects:
- comp-sci-mach-learn
- comp-sci-art-intel
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Data Engineering for the Analysis of Semiconductor Manufacturing Data
type: confpaper
userid: 2175
volume: ~