Predictive intelligence for process correlation modeling
Advances 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 q...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/10356/45878 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Advances 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. |
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