Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model

Electronic packaging has been developed with high resolution and fine interconnection pitches. Non-conductive adhesives (NCAs) have been growing with the increase of I/O pad count and density, along with fine pad bond pitch interconnections. Prediction and optimization of NCA characteristics are inh...

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Bibliographic Details
Published in:Applied sciences 2022-12, Vol.12 (23), p.12150
Main Authors: Min, Kyung-Eun, Jang, Jae-Won, Kim, Jun-Ki, Yi, Sung, Kim, Cheolhee
Format: Article
Language:English
Subjects:
Adhesives
Bond strength
Catalysts
Coupling agents
Curing
Data points
Electronic packaging
Epoxy adhesives
Epoxy resins
formulation
Hardeners
Impact strength
Machine learning
Mass production
Model accuracy
neural network
Neural networks
non-conductive adhesives
Optimization
Packaging
prediction
Predictions
Resins
Semiconductors
Sensors
Shear strength
Shear stress
Shear tests
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Summary:Electronic packaging has been developed with high resolution and fine interconnection pitches. Non-conductive adhesives (NCAs) have been growing with the increase of I/O pad count and density, along with fine pad bond pitch interconnections. Prediction and optimization of NCA characteristics are inherently complicated due to various and extensive materials composing NCAs. In this study, a framework predicting the curing time and shear strength of an NCA is established by a neural network model. NCA formulations with 4 resins, 3 hardeners, 8 catalysts, and a coupling agent were selected from in-house experiments, and an artificial neural network (ANN) with one dense layer with 3 nodes was trained using 65 data points. Model accuracy was improved by 28.9–35.2% compared with the reference, and the trained model was also verified through third-party reference data. Prediction of NCA properties and optimization of NCA formulations for mass production were demonstrated by using the trained ANN model. This paper provides a framework for ANN-based NCA design and confirmed the feasibility of ANN modeling, even with a small dataset.
ISSN:2076-3417
2076-3417
DOI:10.3390/app122312150