摘要
在实际的电子元器件可靠性评估中,通常会遇到小样本的限制,无法满足传统的基于大样本的评估方法的假设.有鉴于此,文中提出了基于支持向量机的小子样元器件可靠性评估方法.该方法通过对元器件失效时间的训练,选择最优的核函数及核参数建立支持向量机模型,利用建立的模型得到拟合直线,从而进行可靠性参数评估.将该方法应用于栅氧化层击穿寿命分布的评估中,可获得比基于大样本的最小二乘评估方法更高的评估精度.
It is difficult to obtain accurate evaluation results in dealing with small-sample electronic devices by using the traditional methods in practice, because the small samplecan not accord with the large sample-based hypothesis of the traditional methods. In order to solve this problem, this paper proposes a reliability evaluation method of small-sample electronic devices based on the support vector machine （SVM）. In this method, after the training of failure time of electronic devices, the optimal kernel function and parameters are selected to construct a SVM model, and the reliability parameters are evaluated according to the straight line fitted by the SVM model. Evaluated results of the life distribution of a gate oxide indicate that the proposed method is more accurate than the least square method based on large sample.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第1期23-26,共4页
Journal of South China University of Technology(Natural Science Edition)
关键词
可靠性
集成电路
支持向量机
栅氧化层
最小二乘法
reliability
integrated circuit
support vector machine
gate oxide
least square method
