Simplified Entropy and Multidimensional Search for Test Points Selection

In order to meet the demand of test points selection on higher speed, a new test points selection algorithm based on simplified entropy and multidimensional search is proposed. With the proposed algorithm, statistic results, instead of the complex entropy calculation, are utilized to estimate the order of entropy. In addition, multidimensional search method is adopted to find potential test points sets that can isolate the potential faults. The proposed method is also adaptive to the complexity of fault dictionary. In each iteration of the proposed algorithm, the dimension of multidimensional search could be changed according to the complexity of fault dictionary. Statistical experiments have shown that the proposed algorithm is more efficient in finding local optimum sets of test points compared with other test points selection algorithms.

Research on Test Points Selection Using Complex Field Fault Modeling in Analog Circuit

In this paper, a new test nodes selection technique based on the complex field fault modeling of analog circuits testability is presented. The function F () by the complex field fault modeling can be used as the fault model, which is applicable to both hard (open or short) and soft (parametric) faults. Therefore, we can obtain the signature curves of the potential fault components by PSPICE and MATLAB. For the testability of fault model, fault-test dependency matrix can be concluded. With the integer-coded fault-wise table method and heuristic graph search algorithm, we can obtain a global minimum node set. The number of potential faults with complex field fault modeling is half compared with the traditional methods and the time complexity of the circuit can be reduced significantly.

Study on Method for Test Points Selection under Uncertainty Based on MBQPSO

Test points choosing are the beginning of optimization of design for testability. With the consideration of uncertain influences on the tests of electronic equipment, the model for design for testability was proposed based on hybrid diagnosis modified by Bayesian network. Based on the new model, the algorithm of MBQPSO was proposed, which could take use of multi-dimension searching mechanism to choose test points according to uncertain correlation matrix between failure modes and tests. With the experiment, the result of this proposed method is closer to the reality and can provide better guidance for future design for testability.