Optimal Test Set Selection for Fault Diagnosis Improvement

Amati, Luca; Bolchini, Cristiana; Salice, F.

doi:10.1109/dft.2011.57

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…In our work, test termination is based on a model that is formulated using characteristics from real defective circuits, thus removing the barrier of abstraction that exists between fault models and actual defects. Moreover, our work complements the test-set compaction [1][2][3][4] and test-response compaction [6] based approaches. In other words, our work can be applied after these procedures to further minimize total cost.…”

Section: Introductionmentioning

confidence: 91%

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Test-data volume optimization for diagnosis

Wang

Poku

et al. 2012

Proceedings of the 49th Annual Design Automation Conference

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Test data collection for a failing integrated circuit (IC) can be very expensive and time consuming. Many companies now collect a fix amount of test data regardless of the failure characteristics. As a result, limited data collection could lead to inaccurate diagnosis, while an excessive amount increases the cost not only in terms of unnecessary test data collection but also increased cost for test execution and data-storage. In this work, the objective is to develop a method for predicting the precise amount of test data necessary to produce an accurate diagnosis. By analyzing the failing outputs of an IC during its actual test, the developed method dynamically determines which failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. The method leverages several statistical learning techniques, and is evaluated using actual data from a population of failing chips and five standard benchmarks. Experiments demonstrate that test-data collection can be reduced by > 30% (as compared to collecting the full-failure response) while at the same time ensuring >90% diagnosis accuracy. Prematurely terminating test-data collection at fixed levels (e.g., 100 failing bits) is also shown to negatively impact diagnosis accuracy.

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Section: Introductionmentioning

confidence: 91%

“…Multi-site testing improves test throughput and thus saves expensive ATE time, although it does not necessarily reduce test-data volume. The other commonly used and effective approach for reducing test-execution cost is test compaction [1][2][3][4]. This area is related since if fewer tests are performed, then less test data is produced.…”

Section: Introductionmentioning

confidence: 99%

Test-data volume optimization for diagnosis

Wang

Poku

et al. 2012

Proceedings of the 49th Annual Design Automation Conference

View full text Add to dashboard Cite

show abstract

“…Golonek [21] studied the genetic algorithm. Yu [22] studied the improving radial basis function network method. Amati [23] studied the integer linear programming method, Wang [24] studied the odd and even space methods, Rahimi [25] studied the geometric method.…”

Section: Introductionmentioning

confidence: 99%

System level test selection based on combinatorial dependency matrix

Yang

Xie²,

Qiu

2021

J. of Syst. Eng. Electron.

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Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators. The existing models and methods are not suitable for system level test selection. The first problem is the lack of detailed data of the units' fault set and the test set, which makes it impossible to establish a traditional dependency matrix for the system level. The second problem is that the system level fault detection rate and the fault isolation rate (referred to as "two rates") are not enough to describe the fault diagnostic ability of the system level tests. An innovative dependency matrix (called combinatorial dependency matrix) composed of three submatrices is presented. The first problem is solved by simplifying the submatrix between the units' fault and the test, and the second problem is solved by establishing the system level fault detection rate, the fault isolation rate and the integrated fault detection rate (referred to as "three rates") based on the new matrix. The mathematical model of the system level test selection problem is constructed, and the binary genetic algorithm is applied to solve the problem, which achieves the goal of system level test selection.

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Research on Analog Integrated Circuit Test Parameter Set Reduction Based on XGBoost

Xiao

Zeng

et al. 2022

J Electron Test

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Optimal Test Set Selection for Fault Diagnosis Improvement

Cited by 5 publications

References 11 publications

Test-data volume optimization for diagnosis

Test-data volume optimization for diagnosis

System level test selection based on combinatorial dependency matrix

Research on Analog Integrated Circuit Test Parameter Set Reduction Based on XGBoost

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