A machine learning approach to explore tensile properties of low-temperature solders

Yu Chen Liu, Ahmad Kholik, Shih Kang Lin

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Low-temperature solder material has been proposed as a solution in reducing the reflow temperature in order to solve the warpage issue in the advanced electronic packaging. Currently, Sn-Bi solder system is considered as a promising material system for low-temperature solder. Nevertheless, (Bi) phase coarsening after thermal aging causes the brittleness of the solder and thus decreases the reliability in real application. Element doping to Sn-Bi solder is typically used in tailoring its properties. However, tailoring properties in the multi-component system with experimental trial-and-error method is not economically feasible. This study employed machine learning approach to explore tensile properties of low-temperature solders.

原文English
主出版物標題2023 International Conference on Electronics Packaging, ICEP 2023
發行者Institute of Electrical and Electronics Engineers Inc.
頁面71-72
頁數2
ISBN(電子)9784991191152
DOIs
出版狀態Published - 2023
事件22nd International Conference on Electronics Packaging, ICEP 2023 - Kumamoto, Japan
持續時間: 2023 4月 192023 4月 22

出版系列

名字2023 International Conference on Electronics Packaging, ICEP 2023

Conference

Conference22nd International Conference on Electronics Packaging, ICEP 2023
國家/地區Japan
城市Kumamoto
期間23-04-1923-04-22

All Science Journal Classification (ASJC) codes

  • 電氣與電子工程
  • 工業與製造工程
  • 材料力學
  • 安全、風險、可靠性和品質
  • 電子、光磁材料

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