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A multiple faults test approach for digital circuits using neural networks

机译:使用神经网络的数字电路多故障测试方法

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A new approach to generate test sets for multiple faults of digital circuits is presented in the paper, which employs neural networks and simulated annealing technique. The neural network models for circuit are built, the test vectors of multiple faults in the circuit can be produced by computing the minimum energy states of the neural networks. An algorithm based on simulated annealing is proposed to compute the minimum states of energy functions, the algorithm has global convergence and has polynomial complexity under a decrement scheme of temperature. Experimental results shows that it is possible to obtain high fault coverage for testable multiple faults with the proposed approach without fault simulation.
机译:本文提出了一种新的生成数字电路多故障测试集的方法,该方法采用了神经网络和模拟退火技术。建立了电路的神经网络模型,可以通过计算神经网络的最小能量状态来生成电路中多个故障的测试向量。提出了一种基于模拟退火的能量函数最小状态计算算法,该算法具有全局收敛性,并且在温度递减的情况下具有多项式复杂度。实验结果表明,采用本文提出的方法无需进行故障仿真,就可以针对可测试的多个故障获得较高的故障覆盖率。

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