Independent test sequence compaction through integer programming

Drineas, Petros; Makris, Yiorgos

doi:10.1109/iccd.2003.1240924

Cited by 34 publications

(27 citation statements)

References 40 publications

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“…Next, we compact the test by selecting the smallest number of these sequences without reducing the coverage. Our compaction is done by an integer linear program (ILP), in a similar way as has been reported in the literature [12,15,27,48].…”

Section: Sequence Compaction and Coveragementioning

confidence: 99%

Spectral RTL Test Generation for Microprocessors

Yogi

Agrawal

2007

20th International Conference on VLSI Design Held Jointly With 6th International Conference on Embedded Systems (VLSID'07)

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We introduce a novel method of test generation for microprocessors at the RTL using spectral methods. Test vectors are generated for RTL faults, which are the stuck-at faults on inputs-outputs of the different modules/registers in the circuit, and the vectors are analyzed using Hadamard matrices for Walsh functions and the random noise level at each primary input. This information then helps generate vector sequences. At the gate-level, a fault simulator and an integer linear program (ILP) compact the test sequences. RTL test appraisal also helps reveal the hard-to-test parts of the circuit. An XOR observability tree was used to improve the testability of those parts. We give results for a simple accumulator-based processor named Parwan. The RTL spectral vectors produced higher coverage in shorter CPU times as compared to a gate-level ATPG.

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Section: Sequence Compaction and Coveragementioning

confidence: 99%

Spectral RTL Test Generation for Microprocessors

Yogi

Agrawal

2007

20th International Conference on VLSI Design Held Jointly With 6th International Conference on Embedded Systems (VLSID'07)

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“…For N = 1, the ILP produces the minimal single-detect test set [6,11,17]. Integer programming has also been applied to sequential circuit test minimization [4]. Following theorems characterize the ILP method:…”

Section: N-detect Test Minimization By Integer Linear Programming (Ilmentioning

confidence: 99%

“…As observed by others [4], the above problem can be converted into a linear programming (LP) problem if we redefine the variables t's as real variables in the range [0.0,1.0]. The LP solution, which can be found in polynomial time and sometimes in linear time, lies in the interior of the hypercube.…”

Section: Randomized Rounding Lp Solutionmentioning

confidence: 99%

“…This is known to be a lower bound for the exact ILP solution, which is 2 in this case. The literature [4,20] gives a randomized rounding method. The real variables t's are treated as probabilities.…”

Section: Randomized Rounding Lp Solutionmentioning

confidence: 99%

“…Our first results evaluate the relative merits of recursive rounding against randomized rounding [4,20] and ILP. Table 1 gives optimized test set sizes for single-detection.…”

Section: Single-detect Testsmentioning

confidence: 99%

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A Reduced Complexity Algorithm for Minimizing N-Detect Tests

Kantipudi

Agrawal

2007

20th International Conference on VLSI Design Held Jointly With 6th International Conference on Embedded Systems (VLSID'07)

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-We give a new recursive rounding linear programming (LP) solution to the problem of N -detect test minimzation. This is a polynomialtime solution that closely approximates the exact but NP-hard integer linear programming (ILP) solution. In ILP, a test is represented by a [0, 1] integer variable and the sum of those variables is minimized. Constraints ensure that each fault has at least N tests with non-zero variables. Traditionally, the problem has been transformed to less complex LP by treating the variables as real numbers, regarded as probabilities with which they can be rounded off to 0 or 1. This is known as the randomized rounding method. In the new method, the LP is recursively used, each time rounding the largest variable to 1 and reducing the size of the LP. The method is found to converge to a solution in just a few LP runs and the result is usually better than that of randomized rounding. Experimental results include ISCAS85 benchmarks and a set of multiplier circuits. N -detect tests for N = 1, 5 and 15 are considered. Also, a 10-vector single-detect sequence for c6288 is given.

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