Physics-Informed Machine Learning for Solder Joint Qualification Tests

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Downloads (Pure)

Abstract

The ability to accurately predict the reliability and lifetime of electronics is of great importance to the industry. The failure of the solder joint is of particular interest for these predictions, because of their susceptibility to failure under thermo-mechanical stress. However, the experimental or even conventional simulation techniques employed to estimate the lifetime of a solder joint are often too expensive or time consuming to be of practical use. Therefore, this work introduces a physics-informed Long Short-Term Memory (LSTM) to predict the plastic strain in the critical area of the solder joint. The predicted values are in agreement with the values gained from finite elements, thereby demonstrating the advantage of applying the proposed methodology.
Original languageEnglish
Title of host publicationProceedings of the 2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)
PublisherIEEE
Number of pages7
ISBN (Electronic)979-8-3503-9363-7
ISBN (Print)979-8-3503-9364-4
DOIs
Publication statusPublished - 2024
Event2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) - Catania, Italy
Duration: 7 Apr 202410 Apr 2024
Conference number: 25th

Publication series

Name2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2024

Conference

Conference2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)
Abbreviated titleEuriSimE 2024
Country/TerritoryItaly
CityCatania
Period7/04/2410/04/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Solder Joint Reliability
  • Plasticity
  • Finite Elements
  • PINN
  • LSTM

Fingerprint

Dive into the research topics of 'Physics-Informed Machine Learning for Solder Joint Qualification Tests'. Together they form a unique fingerprint.

Cite this