Cogprints

Data Engineering for the Analysis of Semiconductor Manufacturing Data

Turney, Peter (1995) Data Engineering for the Analysis of Semiconductor Manufacturing Data. [Conference Paper]

Full text available as:

[img]
Preview
PDF
23Kb

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.

Item Type:Conference Paper
Subjects:Computer Science > Machine Learning
Computer Science > Artificial Intelligence
ID Code:2891
Deposited By:Turney, Peter
Deposited On:16 Apr 2003
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

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.

Metadata

Repository Staff Only: item control page