Open AccessFault recognition based test score for improving the accuracy of defect diagnosis
Author(s) -
Tian Naiyu,
Ouyang Dantong,
Song Jincai,
Zhang Liming
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12195
Subject(s) - fault (geology) , fault coverage , test set , identification (biology) , set (abstract data type) , automatic test pattern generation , computer science , test score , test (biology) , reliability engineering , pattern recognition (psychology) , artificial intelligence , data mining , engineering , statistics , mathematics , electronic circuit , standardized test , paleontology , seismology , geology , biology , programming language , botany , electrical engineering
Diagnosis is an essential step to improve the yield in semiconductor manufacturing industry. It is performed on a failing chip to determine the location of defects. However, the defect diagnosis procedure may obtain too much candidate faults which lead to difficulties in next physical failure analysis. Recently, the methods based on test score to reduce candidate fault set are proved to be effective. The previous test score diagnosis method based on fault free information computes a score for each test which implies the fault identification ability. This letter proposes a novel fault recognition based test score for diagnosis which considers the fault identification capability of tests more comprehensively to improve the original method. More information provided by unique responses and faults detected of a test is added to evaluate the fault recognition for calculating a test score. Then the novel test score which takes more advantages of diagnostic information is applied to adjusting the fault score and reducing the candidate set. The experimental results show that the novel approach reaches smaller fault candidate set than the previous method and increases the diagnostic resolution and precision, and thus provides an effective improvement on the accuracy of diagnosis.