Next Article in Journal
Symmetry Detection in Visual Impairment: Behavioral Evidence and Neural Correlates
Previous Article in Journal
Invisibility and PT Symmetry: A Simple Geometrical Viewpoint
Symmetry 2014, 6(2), 409-426; doi:10.3390/sym6020409
Case Report

The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication

Received: 12 March 2014; in revised form: 16 May 2014 / Accepted: 19 May 2014 / Published: 23 May 2014
View Full-Text   |   Download PDF [398 KB, uploaded 23 May 2014]   |   Browse Figures
Abstract: An innovative classification and back-propagation-network tree (CABPN tree) approach is proposed in this study to estimate the cycle time of a job in a wafer fabrication factory, which is one of the most important tasks in controlling the wafer fabrication factory. The CABPN tree approach is an extension from the traditional classification and regression tree (CART) approach. In CART, the cycle times of jobs of the same branch are estimated with the same value, which is far from accurate. To tackle this problem, the CABPN tree approach replaces the constant estimate with variant estimates. To this end, the cycle times of jobs of the same branch are estimated with a BPN, and may be different. In this way, the estimation accuracy can be improved. In addition, to determine the optimal location of the splitting point on a node, the symmetric partition with incremental re-learning (SP-IR) algorithm is proposed and illustrated with an example. The applicability of the CABPN tree approach is shown with a real case. The experimental results supported its effectiveness over several existing methods.
Keywords: cycle time; estimation; classification and regression tree; symmetric partitioning; back propagation network; wafer fabrication cycle time; estimation; classification and regression tree; symmetric partitioning; back propagation network; wafer fabrication
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Chen, T. The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication. Symmetry 2014, 6, 409-426.

AMA Style

Chen T. The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication. Symmetry. 2014; 6(2):409-426.

Chicago/Turabian Style

Chen, Toly. 2014. "The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication." Symmetry 6, no. 2: 409-426.


Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert