Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/45878
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChan, Ronald Yuen Siang.
dc.date.accessioned2011-06-22T09:24:49Z
dc.date.available2011-06-22T09:24:49Z
dc.date.copyright2011en_US
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/10356/45878
dc.description.abstractAdvances in technology like the miniaturization of electronic devices have caused wafer fabrication to be a competitive field. In order to succeed in the wafer fabrication industry, one way is to increase the process yield. This can be done by decreasing the downtime of machines and increasing the quality of the wafers. The objective of this project is to familiarize with the different neural networks and determine their viability for use for use in reducing the downtime of wafer fabrication processes. As such, the downtime required for machine maintenance can be reduced and quality of processed wafers increased. The first part of the project is to perform pre-processing on data collected from wafer fabrication machines according to the dates the data were collected. After which the data will be processed using the Peltarion Synapse software for the design and training of artificial neural networks. The performance of the network will then be evaluated based on the Mean Square Error of the output.en_US
dc.format.extent52 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titlePredictive intelligence for process correlation modelingen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorEr Meng Jooen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
dc.contributor.organizationA*STAR SIMTechen_US
dc.contributor.supervisor2Li Xiangen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
eB4030101.pdf
  Restricted Access
3.14 MBAdobe PDFView/Open

Page view(s)

424
Updated on Nov 5, 2024

Download(s)

7
Updated on Nov 5, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.