一种基于BP神经网络的集成电路PHM模型

A prognostics and health management model for integrated circuits based on back propagation neural network

提出了一种基于数据驱动的集成电路故障预测与健康管理(PHM)模型,该模型基于反向传播(BP)神经网络算法,避免了对集成电路老化失效物理机理的依赖,能有效拟合集成电路失效的非线性函数关系。以已编程应用设计的FPGA为目标器件,通过实验提取参数样本进行模型训练,并将模型应用于实测验证。结果表明,该模型输出结果与实测结果吻合良好,能有效满足集成电路故障预测与健康管理的实际应用。

We propose a prognostics and health management （PHM） model for integrated circuits （ICs） based on back propagation （BP） neural network. The model is not only independent of physical mechanism of ICs aging, hut can effectively fit the non-linear function of IC failures as well. We conduct a large number of experiments on a programmed FPGA, and take the extracted experimental parameter samples as training samples to train the PHM model. Experimental results verify the trained model. The results show that the proposed PHM model is in agreement with the experiment and can meet the requirements of PHM for ICs.