There are many techniques for analyzing IC fails, but they are
scattered over the professional IC test and diagnosis literature,
and in various statistics and data mining handbooks. Moreover, many
data mining techniques that are standard in other data analysis
environments, and that are appropriate for analyzing IC fails, have
not yet been employed for that purpose.
Data Mining and Diagnosing IC Fails addresses the problem of
obtaining maximum information from (functional) integrated circuit
fail data about the defects that caused the fails. It starts at the
highest level from mere sort codes, and drills down via various
data mining techniques to detailed logic diagnosis. The various
approaches discussed in this book have a thorough theoretical
underpinning, but are geared towards applications on real life fail
data and state of the art ICs. This book brings together a large
number of analysis techniques that are suitable for IC fail data,
but that are not available elsewhere in a single place. Several of
the techniques, in fact, have been presented only recently in
technical conferences.
The purpose of the book is to bring together in one place a
large number of analysis, data mining and diagnosis techniques that
have proven to be useful in analyzing IC fails. The descriptions of
the techniques and analysis routines is sufficiently detailed that
professional manufacturing engineers can implement them in their
own work environment.
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