[1]易茂祥,宋晨钰,于金星,等.基于随机森林的集成电路适应性测试方研究[J].郑州大学学报(工学版),2021,42(04):13-18.[doi:10.13705/j.issn.1671-6833.2021.02.016]
　Yi Maoxiang,Song Chenyu,Yu Jinxing,et al.An Adaptive Test Method of IC Based on Random Forest[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):13-18.[doi:10.13705/j.issn.1671-6833.2021.02.016]
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基于随机森林的集成电路适应性测试方研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年04期
页码:
13-18
栏目:
出版日期:
2021-07-30

文章信息/Info

Title:
An Adaptive Test Method of IC Based on Random Forest
作者:
易茂祥; 宋晨钰; 于金星; 宋钛; 鲁迎春; 黄正峰
合肥工业大学电子科学与应用物理学院;
Author(s):
Yi Maoxiang; Song Chenyu; Yu Jinxing; Song Titan; Lu Yingchun; Huang Zhengfeng;
School of Electronic Science and Application Physics of Hefei University of Technology;
关键词:
Keywords:
IC test; random forest; adaptive test; test time; test cost
DOI:
10.13705/j.issn.1671-6833.2021.02.016
文献标志码:
A
摘要:
随着集成电路产业的高速发展，芯片的复杂度不断增加，不可避免地导致高测试成本问题。适应性测试为降低测试成本提供了一种解决方案。提出了一种基于随机森林的适应性测试方法，利用特征重要性筛选出最为重要的测试组，同时根据测试组所能测出缺陷芯片的个数对测试组进行排序，通过删除部分测试组来降低测试时间。实验结果表明，随机森林与KNN和逻辑回归相比始终保持较低的测试逃逸水平。与现有测试方法相比，测试组删除可以大幅降低测试时间，从而降低测试成本。
Abstract:
An adaptive test method based on random forest was proposed to solve the problem of high test cost caused by increasing test time.For the chip of the training model,by calculating the Gini index,the importance of each test group to the model classification in the process of the chip parameter test was obtained,and the feature importance was used to quantify it.Then the importance of the test group was ranked to screen out the most important test groups for predicting the quality of the chip.At the same time,the number of defective chips that could be detected in each test group was counted.Part of the test was performed on the chips in the test set,the test time was reduced by deleting some test groups,and the random forest algorithm was used to predict the quality of the chips,so as to achieve a compromise between prediction accuracy and less time.Experimental results showed that compared with KNN and logistic regression algorithms,random forest could always maintains the best prediction accuracy,test escape level and running time.Compared with traditional test methods,the test time could be reduced by about 28% while maintaining a low test escape level.Compared with the other two representative adaptive testing methods,the proposed method performed better in reducing the test time.

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更新日期/Last Update: 2021-08-26