Proc. SPIE. 5044, Advanced Process Control and Automation
KEYWORDS: Data modeling, Control systems, Data processing, Signal processing, Process control, Algorithm development, Computer architecture, Autoregressive models, Statistical modeling, Process modeling
Process control in the fab today employs a wide range of techniques to gather data, monitor processes and adjust through
feed-forward and feed-back. This paper proposes that many substantial benefits could be derived from a broad
abstraction of process control statistics algorithms as well as of data collection and distribution, in a manner parallel to how software users benefit from object oriented concepts. The abstracted algorithmic approach is based on statistics fundamentals.
The paper first defines abstraction and discusses the benefits of its application to process control. It then defines a statistics experiment to test EWMA as one example of how a popular contemporary process control practice can misbehave when faced with four specific data attributes. The experiment quantifies the limitations of EWMA, and indicates that its performance is greatly enhanced when the more fundamental approach pre-processes its data. EWMA is not being singled out results are generalizable to other methods. The last two sections summarize findings and draw conclusions.