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Iterative machine learning-aided framework bridges between fatigue and creep damages in solder interconnections

Samavatian, V ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/TCPMT.2021.3136751
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this paper, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforces the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network to fulfill the data diversity. Outcomes show a very significant enhancement in lifetime prediction accuracy of the solder joint within a very short time. The effects of solder alloy and solder layer geometry are separately evaluated on the creep-fatigue damage evolution of the solder joint. The results reveal that Sn-Ag-Cu based solder alloy generally has better performance. Moreover, the creep and fatigue damage evolutions are found dominant, respectively in Sn-Pb and Sn-Ag-Cu based solder alloy. The proposed framework offers a tool allowing for reliability-driven design of electronic devices in the early-stage of fabrication. IEEE
  6. Keywords:
  7. Artificial intelligence ; Binary alloys ; Copper alloys ; Creep ; Geometry ; Iterative methods ; Lead alloys ; Lead-free solders ; Learning systems ; Reliability ; Soldering ; Thermoelectric equipment ; Tin alloys ; Creep damages ; Damage evolution ; Electronics devices ; Iterative machine learning ; Prediction algorithms ; Predictive models ; Self-healing ; Solder alloys ; Solder joints ; Thermal loadings ; Forecasting
  8. Source: IEEE Transactions on Components, Packaging and Manufacturing Technology ; 2021 ; 21563950 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9656134