Machine Learning based Scan Chain Diagnosis for Double Faults

被引：0
|
作者：
Yun, Hyojoon [1 ]
Kim, Taehyun [1 ]
Kang, Sungho [1 ]
机构：
[1] Yonsei Univ, Dept Elect & Elect Engn, Seoul, South Korea
来源：
2023 20TH INTERNATIONAL SOC DESIGN CONFERENCE, ISOCC | 2023年
基金：
新加坡国家研究基金会;
关键词：
scan chain diagnosis; double faults; machine learning; IC;
D O I：
10.1109/ISOCC59558.2023.10396107
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The importance of scan chain diagnosis for improving yield is increasing as the complexity of semiconductor circuits increases. The conventional scan chain diagnosis methods provide sufficient diagnostic accuracy for a single fault and double faults. However, if the backward fault in double faults masks the other fault during the load, it is difficult to obtain sufficiency accuracy because fails in good chains are masked. To solve this problem, machine learning based scan chain diagnosis for double faults is proposed. Experimental results show that the proposed method achieves improved diagnostic accuracy for double faults.
引用
收藏
页码：341 / 342
页数：2
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