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- Title
Deep neural network aided cohesive zone parameter identifications through die shear test in electronic packaging.
- Authors
Zhao, Libo; Dai, Yanwei; Wei, Jiahui; Qin, Fei
- Abstract
The die shear test is a feasible and conventional method to characterize the shear strength of die‐attaching layer materials in electronic packaging. A new method for determining cohesive zone model (CZM) parameters using deep neural networks (DNN) and die shear tests is proposed, different from classical fracture framework or lap shear test‐based methods. With the sintered nano‐silver die shear test, the results show that the bilinear CZM inversion results agree well with the experimental results. It is found that the DNN model has high accuracy in predicting and identifying the maximum shear traction strength τmax, separation displacement of the interface δf, and the interface stiffness k1 of CZM parameters for sintered nano‐silver adhesive layer through die shear test load versus displacement curves. The presented DNN‐aided inverse identifying method through the die shear test in this paper could provide an alternative and convenient method for extracting CZM parameters of various kinds of adhesive materials in electronic packaging. Highlights: Die shear tests were used for the inverse identification of CZM parameters.The die shear test P–δ curves were established as the dataset.A DNN‐aided CZM inverse identification method was proposed.The DNN‐aided model can accurately identify the CZM parameters.
- Subjects
ARTIFICIAL neural networks; ELECTRONIC packaging; PARAMETER identification; COHESIVE strength (Mechanics); PACKAGING materials; SHEAR strength; DEEP learning
- Publication
Fatigue & Fracture of Engineering Materials & Structures, 2024, Vol 47, Issue 3, p766
- ISSN
8756-758X
- Publication type
Article
- DOI
10.1111/ffe.14220