Sn-based solder design using machine learning approach

Yu Chen Liu, Chih Han Yang, Shih Kang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Sn-based solder is a typical material for electronic interconnections. Low-Ag-content Sn-based solder exhibits a low hardness and is promising for avoiding the drop-induced failure. However, Sn-based solder has a wide range of composition and thus modulating its hardness via experimental trial-and-error method is not economically feasible. In this study, we employed the machine learning approach to design low-hardness Sn-based solders. We successfully designed an alloy that showed a hardness value lower than pure Sn, which has the potential of having a high-resistance to drop-induced failure.

Original languageEnglish
Title of host publication2022 International Conference on Electronics Packaging, ICEP 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages43-44
Number of pages2
ISBN (Electronic)9784991191138
DOIs
Publication statusPublished - 2022
Event21st International Conference on Electronics Packaging, ICEP 2022 - Sapporo, Japan
Duration: 2022 May 112022 May 14

Publication series

Name2022 International Conference on Electronics Packaging, ICEP 2022

Conference

Conference21st International Conference on Electronics Packaging, ICEP 2022
Country/TerritoryJapan
CitySapporo
Period22-05-1122-05-14

All Science Journal Classification (ASJC) codes

  • Process Chemistry and Technology
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Electronic, Optical and Magnetic Materials

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