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Appendix A
The architecture and
methodology used in our FAKS system can provide a solution for the
features mentioned in the following article. The following paragraphs are quoted from SOLID
STATE TECHNOLOGY Article Date November, 1999 Magazine Volume 42 Issue
11 Title The Emerging Role For Data Mining. “Data mining can assist
in solving new problems by looking for commonalties in the processing history
of previous occurrences — for example, diagnosing excursions of in-line
defect metrology and wafer electrical test (WET) data. Increasingly, however,
methods will be developed to reduce these limitations by increasing the
breadth of models that can be developed and by reducing the frequency of
false signals. Eventually it will be
possible to couple data mining to repositories of knowledge as well as data.
This will lead to self-optimizing systems of increasing complexity as
witnessed by the emerging trend of new offerings for work cell management
from the major tool suppliers. This trend will continue and lead to larger
centers of automation that will rely on data mining to anticipate and recover
from potential problems. The rate at which this vision is achieved will hinge
on the development and adoption of standards for data storage and access
between and within tools. Such standards will also need to be extended to the
more difficult topic of knowledge storage. Scripting languages will need to
be developed or enhanced to manage the complex interactions among tool, cell,
and factory control systems and data-mining engines. Data-mining technology
presents an opportunity to increase significantly the rate at which the
volumes of data generated on the manufacturing process can be turned into
information. Truly, its time has come! “ Knowledge-based
systems Knowledge-based systems,
such as expert systems, signature analysis, and classification trees,
encapsulate knowledge capable of being derived either whole or in part by
data mining. Expert systems create hierarchical knowledge systems given a set
of rules. They guide a user through a decision
making or diagnostic process. Many have been built following in-depth interviews
with experts. Data mining provides another method of eriving rules for expert
systems. Signature analysis is specifically designed to assimilate clues
associated with diagnostic data to fingerprint a process failure. Data mining
can be used to discover patterns that associate a given failure with a set of
process conditions. Once associations are known, they can be applied to new
data through signature analysis to implicate likely process conditions that
led to the failure. Classification trees are a special case of data mining
when the response variable is
categorical. They can be built with or without use of data-mining technology
if the knowledge can be obtained through other means. |