Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics.

ABSTRACT: The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint thickness on the reliability of electronic devices; we will illustrate that the thermal cycling variat

SUBMITTER: Samavatian V 

PROVIDER: S-EPMC7481227 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature