论文标题
HYBMT：基于混合元预测的ML算法，用于快速测试矢量产生
HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation
论文作者
论文摘要
ML模型越来越多地用于增加测试覆盖范围并减少整体测试时间。该领域仍处于新生的阶段，到目前为止，还没有在大型电路的速度和准确性方面匹配或超过商业工具的算法。我们在本文中提出了一种ATPG算法HYBMT，该算法最终打破了这一障碍。像姐妹方法一样，我们增强了使用递归回溯的经典podem算法。我们设计了一个自定义的2级预测指标，该预测可以预测逻辑门的输入网，其值需要设置以确保输出为给定值（0或1）。我们的预测因子从两个一级预测因子中选择了输出，其中最有效的是定制神经网络，而另一个是SVM回归器。与流行的最先进的商业ATPG工具相比，HYBMT在CPU时间内的总体下降幅度为56.6％，而不会损害EPFL基准电路的故障覆盖率。 HYBMT还显示出比最佳基于ML的算法的速度为126.4％，同时获得了EPFL基准电路相等或更好的故障覆盖率。
ML models are increasingly being used to increase the test coverage and decrease the overall testing time. This field is still in its nascent stage and up till now there were no algorithms that could match or outperform commercial tools in terms of speed and accuracy for large circuits. We propose an ATPG algorithm HybMT in this paper that finally breaks this barrier. Like sister methods, we augment the classical PODEM algorithm that uses recursive backtracking. We design a custom 2-level predictor that predicts the input net of a logic gate whose value needs to be set to ensure that the output is a given value (0 or 1). Our predictor chooses the output from among two first-level predictors, where the most effective one is a bespoke neural network and the other is an SVM regressor. As compared to a popular, state-of-the-art commercial ATPG tool, HybMT shows an overall reduction of 56.6% in the CPU time without compromising on the fault coverage for the EPFL benchmark circuits. HybMT also shows a speedup of 126.4% over the best ML-based algorithm while obtaining an equal or better fault coverage for the EPFL benchmark circuits.
