https://scholars.lib.ntu.edu.tw/handle/123456789/580670
標題: | Chip Performance Prediction Using Machine Learning Techniques | 作者: | Su M.-Y Lin W.-C Kuo Y.-T Li C.-M Fang E.J.-W Hsueh S.S.-Y. CHIEN-MO LI |
關鍵字: | Automation; Equipment testing; Forecasting; Predictive analytics; Turing machines; VLSI circuits; Automation tests; Chip performance; Functional test; Machine learning techniques; Prediction accuracy; Process Variation; Ring oscillator; Structural tests; Machine learning | 公開日期: | 2021 | 來源出版物: | 2021 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2021 - Proceedings | 摘要: | Process variation cause a big variation on chip performance, so we need to apply expensive functional test to do the speed binning. In this work, we propose a machine learning-based chip performance prediction framework. We only consider on-chip ring oscillator's frequency as feature, which can be obtained from structural test. We select most important cells for ring oscillators at pre-silicon stage, so we can minimize the ring oscillators on the chip. Experimental results on 12K industry chips show that our prediction accuracy is comparable to automation test equipment's measurement according to company's criterion. ? 2021 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106566003&doi=10.1109%2fVLSI-DAT52063.2021.9427338&partnerID=40&md5=941d6f0f6ad68a394d63a357d040f753 https://scholars.lib.ntu.edu.tw/handle/123456789/580670 |
DOI: | 10.1109/VLSI-DAT52063.2021.9427338 |
顯示於: | 電機工程學系 |
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