With the constant improvement of the scale and complexity of integrated circuit,how to efficiently generate test vector is becomed the key to digital circuit board fault detection. Based on the analysis of the automatic test vector generation questions,the mathematical model of circuit under test by using neural network is built,which convert the test vector generation problems into math problems,and puts forward a kind of efficient particle swarm optimization algorithm for solving the problems. VC+ + programming tool is used to realize the proposed approach. Some of the international standard circuits ISCAS ˊ85 are carried on the experiment. The experiment data shows that the fault coverage is up to 100%,the test time for small-scale single-fault circuit is reduced by 13% compared with reference,the large-scale circuit test time reduction of 61%,and the larger the circuit scale and reduce the time more obvious.%随着集成电路的规模和复杂度的不断提高，高效地生成测试矢量已成为数字电路板故障检测的关键所在。在对测试向量自动生成问题分析的基础上，利用神经网络对被测电路进行数学建模，将测试矢量生成转化为数学问题，提出了一种高效求解该问题的粒子群优化算法。用VC++对所提出的方法进行编程实现，并对ISCAS’85国际标准电路中的一些电路进行了实验。实验数据表明，故障覆盖率达到了100%，对于小规模电路单故障的测试时间与有关文献相比减少了13%，规模相对较大的电路的测试时间减少了61%，而且电路规模越大，时间的减少就越明显。
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