@inproceedings{f00b2c0c46d94706b391b16069ff5612,
title = "Fast and Accurate Current Prediction in Packages Using Neural Networks",
abstract = "Electromigration (EM) has become one major reliability concern in modern integrated circuit (IC) packages. EM is caused by large currents flowing in metals and the resulting mean time to failure (MTTF) is highly dependent on the maximum current value. We here propose a scheme for fast and accurate prediction of the maximum current on the ball grid arrays (BGAs) in a package given the pin current information of the die. The proposed scheme uses neural networks to learn the resistance network of the package and achieve the non-linear current mapping. The fast prediction tool can be used for analysis and design exploration of the pin assignment on the die level.",
keywords = "BGA, Black's equation, Electromigration, MTTF, machine learning, neural networks, packages",
author = "Yanan Liu and Tianjian Lu and Kim, {Jin Y.} and Ken Wu and Jin, {Jian Ming}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019 ; Conference date: 22-07-2019 Through 26-07-2019",
year = "2019",
month = jul,
doi = "10.1109/ISEMC.2019.8825314",
language = "English (US)",
series = "2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "621--624",
booktitle = "2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019",
address = "United States",
}