[PDF] Test-data volume optimization for diagnosis | Semantic Scholar (2024)

This work presents a methodology to minimize the amount of fail data that is provided to the diagnosis procedure without compromising the diagnosis accuracy (DA).

The volume of fail data is addressed by considering the test set that is used for collecting fail data by observing that certain faults from a set of target faults produce significantly larger numbers of faulty output values than other faults under a given test set.

Test reordering targets logic defects based on information that is derived during defect diagnosis and indicates that test reordering helps to terminate fail data collection early without impacting the diagnosis quality.

This article describes a procedure for modifying a stored test set to reduce the fail data volume under a test data compression method where a linear-feedback shift-register is used for on-chip decompression.

A new algorithm is described for dynamically selecting the test pattern order that leads to significant reduction in memory cost, and uses as little as 1/500 of tester space to store failing data.

The algorithm was implemented in a test program library and integrated into a production fail flow for sort data-log optimization, and yielded a 5x reduction in the test data trasfer time.

This article describes a logic diagnosis procedure, from the class of procedures used by commercial tools, that addresses the challenge of balancing the use of the two types of data to produce accurate logic diagnosis results.

This article considers the selection of the partially specified fault-free output response a priori and presents experimental results to demonstrate that the fail data volume is reduced significantly, and accurate logic diagnosis is possible with the reduced fail data.

A number of metrics based on information theory that may help in selecting failure logs dynamically for improving the accuracy and resolution of final diagnosis are proposed.

The article describes a test generation procedure for bounded transparent scan that uses only pass/fail data, and targets the same diagnostic quality as a conventional scan-based test set with full fail data.

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To use techniques for reducing the size of fault pairs to be considered at a time, to use novel variants of the fault distinguishing table method for combinational circuits and reverse order restoration method for sequential circuits, and to introduce heuristics to manage the space complexity of considering all fault pairs for large circuits.

This work proposes a framework to enable fast and accurate defect simulation, by making use of existing and well-developed mixed-signal simulation technology (traditionally used for design verification), and demonstrates that the proposed framework is scalable to handle large designs efficiently.

The proposed methodology can eliminate an expensive mechanical test for a commercially available accelerometer with little error and it's possible to completely eliminate the error (for failing parts) using specification guard banding.

The proposed methodology enables seamless reuse of the existing standard ATPG based diagnosis infrastructure with compressed test data and indicates that the diagnostic resolution of devices with embedded compression is comparable with that of devices without embedded compression.

Results from several simulated and over 800 failing ICs reveal a significant improvement in localization and an accurate model of the logic-level defect behavior that provides useful insight into the actual defect mechanism.

An integer linear program (ILP) formulation using a fault diagnostic table is given and generalized fault independence is defined, which identifies many fault pairs that are guaranteed to be distinguished, significantly reducing the number of ILP constraints.

This paper focuses on the evolution of the BBN nodes probabilities, to define a stop criterion to interrupt the diagnosis process when additional test outcomes would not provide further useful information for identifying the faulty candidate.

The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.

Van Driesche RG, Hoddle M, and Center T (2008) Control of Pests and Weeds by Natural Enemies, an Introduction to Biological Control.

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