https://scholars.lib.ntu.edu.tw/handle/123456789/630398
標題: | Low-IR-Drop Test Pattern Regeneration Using A Fast Predictor | 作者: | Liu, Shi Tang Chen, Jia Xian Wu, Yu Tsung Hsieh, Chao Ho CHIEN-MO LI Chang, Norman Li, Ying Shiun Chuang, Wen Tze |
關鍵字: | IR-drop | machine learning | test pattern | 公開日期: | 1-一月-2022 | 卷: | 2022-April | 來源出版物: | Proceedings - International Symposium on Quality Electronic Design, ISQED | 摘要: | IR-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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/630398 | ISBN: | 9781665494663 | ISSN: | 19483287 | DOI: | 10.1109/ISQED54688.2022.9806245 |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。