Study on prediction models for integrated scheduling in semiconductor manufacturing lines
2012
Quality prediction of lot operations is significant for integrated scheduling
in semiconductor production line. The modeltraining algorithm needs to be
fast and incremental to satisfy the online applications where data comes one
by one or chunk by chunk. This paper presents a novel prediction model
referred to as Incremental Extreme Least Square Support Vector Machine
(IELSSVM), which transforms the data into ELM feature space and then
minimizes the structural risk like LSSVM. The transformation into ELM feature
space can be regarded as a good dimensionality reduction. The incremental
formula is proposed for on-line industrial application to avoid retraining
when data comes one by one or chunk by chunk. Detailed comparisons of the
IELSSVM algorithm with other incremental algorithms are achieved by
simulation on benchmark problems and real overlay prediction problem of
lithography in semiconductor production line. The results show that IELSSVM
has better performance than other incremental algorithms like OS-ELM.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
0
References
0
Citations
NaN
KQI