Title
Cluster based analytical method for the lot delivery forecast in semiconductor fab with wide product range
Abstract
The usual forecast method in semiconductor industry is simulation. Due to the manufacturing environment, the number of processes and the multitude of disturbing factors the development of high-fidelity simulation model is time-consuming and requires a huge amount of high quality basic data. The simulation facilitates a detailed prediction possible, but in many cases this level of detail of the forecast information is not required. In this paper, we present an alternative forecast method. It is considerably faster and the results for a subset of parameters are comparable to simulation. The solution does not need a complete fab model but a limited mathematical system and some fast algorithms which make the forecast of important parameters or characteristics possible. The prediction is based completely on statistics extracted from historical lot data traces. It is already implemented and tested in a real semiconductor fab environment and we also present some validation results.
Year
DOI
Venue
2011
10.1109/WSC.2011.6147897
Winter Simulation Conference
Keywords
Field
DocType
forecast information,complete fab model,high-fidelity simulation model,wide product range,lot delivery forecast,basic data,alternative forecast method,real semiconductor fab environment,usual forecast method,historical lot data trace,manufacturing environment,detailed prediction,analytical method,data model,simulation model,level of detail,prediction algorithms,manufacturing,semiconductor device modeling,parameter estimation,statistical analysis,data models,mathematical model
Data modeling,Semiconductor device modeling,Level of detail,Simulation,Computer science,Prediction algorithms,Semiconductor fab,Estimation theory,Semiconductor industry,Statistical analysis
Conference
ISSN
ISBN
Citations 
0891-7736
978-1-4799-2077-8
1
PageRank 
References 
Authors
0.36
4
5
Name
Order
Citations
PageRank
Marcin Mosinski110.70
Daniel Noack2273.76
Falk Stefan Pappert342.18
Oliver Rose41710.43
Wolfgang Scholl5628.06