Liu, Shi TangShi TangLiuChen, Jia XianJia XianChenWu, Yu TsungYu TsungWuHsieh, Chao HoChao HoHsiehCHIEN-MO LIChang, NormanNormanChangLi, Ying ShiunYing ShiunLiChuang, Wen TzeWen TzeChuang2023-04-202023-04-202022-01-01978166549466319483287https://scholars.lib.ntu.edu.tw/handle/123456789/630398IR-drop becomes an important issue for testing in advanced technology nodes. In this paper, we propose a low-IR-drop test pattern regeneration to produce IR-drop-safe patterns. To speed up IR-drop analysis, we apply an existing machine learning model to predict IR-drop of test patterns. Because we already know the IR-drop of test patterns, we learn from test patterns to determine low-IR-drop preferred values and extract important bit assignments. By applying our techniques, we regenerate test patterns without predicted IR-drop violations. Experimental results show that our test length overhead is only 2.37% on average, and there is no fault coverage loss. Finally, we perform accurate IR-drop simulation on 10 IR-drop-safe patterns and no IR-drop violations are found.IR-drop | machine learning | test patternconference paper10.1109/ISQED54688.2022.98062452-s2.0-85133780249https://api.elsevier.com/content/abstract/scopus_id/85133780249