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ACM Special Interest Group on Knowledge
Discovery & Data Mining
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KDD-2000
Sixth ACM SIGKDD International Conference on
Knowledge Discovery & Data Mining
August 20-23, 2000
Boston, MA, USA
KDD-2000
Best Application Paper
Mining
IC Test Data to Optimize VLSI Testing
Tony
Fountain (San Diego Supercomputer Center, UCSD)
Thomas
Dietterich (Oregon State University)
Bill
Sudyka (Hewlett Packard Company)
Abstract:
We
describe an application of data mining and decision analysis to the problem of
die-level functional test in integrated circuit manufacturing. Integrated
circuits are fabricated on large wafers that can hold hundreds of individual
chips ("die"). In current practice, large and expensive machines
test each of these die to check that they are functioning properly (die-level
functional test; DLFT), and then the wafers are cut up, and the good die are
assembled into packages and connected to the package pins. Finally, the
resulting packages are tested to ensure that the final product is functioning
correctly. The purpose of die-level functional test is to avoid the expense of
packaging bad die and to provide rapid feedback to the fabrication process by
detecting die failures. The challenge for a decision-theoretic approach is to
reduce the amount of DLFT (and the associated costs) while still providing
process feedback. We describe a decision-theoretic approach to DLFT in which
historical test data is mined to create a probabilistic model of patterns of
die failure. This model is combined with greedy value-of-information
computations to decide in real time which die to test next and when to stop
testing. We report the results of several experiments that demonstrate the
ability of this procedure to make good testing decisions, good stopping
decisions, and to detect anomalous die. Based on experiments with historical
test data from Hewlett Packard Company, the resulting system has the potential
to improve profits on mature IC products.
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