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Automatic test pattern generation

About: Automatic test pattern generation is a research topic. Over the lifetime, 8214 publications have been published within this topic receiving 140773 citations. The topic is also known as: ATPG.


Papers
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Proceedings ArticleDOI
06 Nov 1994
TL;DR: Using the technique presented here an efficient static test set for analog and mixed-signal ICs can be constructed, reducing both the test time and the packaging cost.
Abstract: Static tests are key in reducing the current high cost of testing analog and mixed-signal ICs. A new DC test generation technique for detecting catastrophic failures in this class of circuits is presented. To include the effect of tolerance of parameters during testing, the test generation problem is formulated as a minimax optimization problem, and solved iteratively as successive linear programming problems. An analytical fault modeling technique, based on manufacturing defect statistics is used to derive the fault list for the test generation. Using the technique presented here an efficient static test set for analog and mixed-signal ICs can be constructed, reducing both the test time and the packaging cost.

86 citations

Journal ArticleDOI
TL;DR: Test case selection in model‐based testing is discussed focusing on the use of a similarity function, and it is shown that similarity‐based selection can be more effective than random selection when applied to automatically generated test suites.
Abstract: Test case selection in model-based testing is discussed focusing on the use of a similarity function. Automatically generated test suites usually have redundant test cases. The reason is that test generation algorithms are usually based on structural coverage criteria that are applied exhaustively. These criteria may not be helpful to detect redundant test cases as well as the suites are usually impractical due to the huge number of test cases that can be generated. Both problems are addressed by applying a similarity function. The idea is to keep in the suite the less similar test cases according to a goal that is defined in terms of the intended size of the test suite. The strategy presented is compared with random selection by considering transition-based and fault-based coverage. The results show that, in most of the cases, similarity-based selection can be more effective than random selection when applied to automatically generated test suites. Copyright © 2009 John Wiley & Sons, Ltd.

86 citations

Journal ArticleDOI
TL;DR: A novel method to automatically generate test cases based on UML state models is presented, which is fully automatic and the generated test cases satisfy transition path coverage criteria.
Abstract: UML is widely accepted and used by industry for modelling and design of software systems. A novel method to automatically generate test cases based on UML state models is presented. In the present approach, the control and data flow logic available in the UML state diagram to generate test data are exploited. The state machine graph is traversed and the conditional predicates on every transition are selected. Then these conditional predicates are transformed and function minimisation technique is applied to generate test cases. The present test data generation scheme is fully automatic and the generated test cases satisfy transition path coverage criteria. The generated test cases can be used to test class as well as cluster-level state-dependent behaviours.

86 citations

Journal ArticleDOI
TL;DR: This paper presents a technique that applies structural knowledge about the circuit during the transformation of Boolean SAT solvers and shows that the size of the problem instances decreases, as well as the run time of the ATPG process.
Abstract: Due to the rapidly growing size of integrated circuits, there is a need for new algorithms for automatic test pattern generation (ATPG). While classical algorithms reach their limit, there have been recent advances in algorithms to solve Boolean Satisfiability (SAT). Because Boolean SAT solvers are working on conjunctive normal forms (CNFs), the problem has to be transformed. During transformation, relevant information about the problem might get lost and, therefore, is not available in the solving process. In this paper, we present a technique that applies structural knowledge about the circuit during the transformation. As a result, the size of the problem instances decreases, as well as the run time of the ATPG process. The technique was implemented, and experimental results are presented. The approach was combined with the ATPG framework of NXP Semiconductors. It is shown that the overall performance of an industrial framework can significantly be improved. Further experiments show the benefits with regard to the efficiency and robustness of the combined approach.

86 citations

Proceedings ArticleDOI
18 Dec 2009
TL;DR: A new methodology to significantly increase the defect coverage of the test patterns generated by ATPG tools is presented, which directly targets the actual root causes of intra-cell defects.
Abstract: Industry is facing increasingly tougher quality requirements for more complex ICs. To meet these quality requirements we need to improve the defect coverage. This paper presents a new methodology to significantly increase the defect coverage of the test patterns generated by ATPG tools. The fault model used during the ATPG is enhanced to directly target layout-based intra-cell faults. In contrast to previous techniques, such as Gate-Exhaustive, N-Detect, or Embedded-Multi-Detect, which either are too complex for real-world designs or merely improve the probability of detecting intra-cell defects, the new approach targets the actual root causes of intra-cell defects. The newly proposed Cell-Aware-methodology has been evaluated for 90nm and 65nm technologies on 1671 library cells and on 10 real industrial designs with up to 50 million faults. The experimental results show an average increase of 1.2% in defect coverage and a reduction of 420ppm in escape rate for a 50mm2 design.

86 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202338
202278
202125
202048
201980
201869