S7 - CNN-based Data-Model Co-Design for Efficient Test-termination Prediction
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hdl:2117/372156
Document typeConference report
Defense date2022-05
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Abstract
Failure diagnosis is a software-based data-driven
procedure. Collecting an excessive amount of fail data not only
increases the overall test cost, but may also lead to degradation
of diagnostic resolution. Test-termination prediction is thus
proposed to dynamically determine which failing test pattern
to terminate testing, producing an amount of test data that
is sufficient for an accurate diagnosis analysis. In this work,
we describe a novel data-model co-design method of using
deep learning method for efficient test-termination prediction.
In particular, images describing the failing test responses are
constructed from failure-log files. A multi-layer convolutional
neural network (CNN) embedding a residual block is then
trained, based on the images and known diagnosis results. The
learned CNN model is later deployed in a test flow to determine
the optimal test-termination for an efficient and quality diagnosis.
Experiments on actual failing chips and standard benchmarks
demonstrate that the proposed method outperforms SOTA works.
Our method creates opportunities to harness the power of deep
learning for improving diagnostic efficiency and quality.
CitationWang, H.; Wu, Z.; Liu, W. S7 - CNN-based Data-Model Co-Design for Efficient Test-termination Prediction. A: 27th IEEE European Test Symposium (ETS). 2022,
Publisher versionhttps://ieeexplore.ieee.org/xpl/conhome/9810327/proceeding
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S7-1.pdf | 1,333Mb | Restricted access |