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GA-BP in Thermal Fatigue Failure Prediction of Microelectronic Chips

1
School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
2
Department of Engineering Mechanics, China University of Petroleum (East China), Qingdao 266580, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 542; https://doi.org/10.3390/electronics8050542
Received: 13 April 2019 / Revised: 8 May 2019 / Accepted: 10 May 2019 / Published: 14 May 2019
(This article belongs to the Section Microelectronics and Optoelectronics)
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Abstract

A thermal fatigue life prediction model of microelectronic chips based on thermal fatigue tests and solder/substrate interfacial singularity analysis from finite element method (FEM) analysis is established in this paper. To save the calculation of interfacial singular parameters of new chips for life prediction, and improve the accuracy of prediction results in actual applications, a hybrid genetic algorithm–artificial neural network (GA–ANN) strategy is utilized. The proposed algorithm combines the local searching ability of the gradient-based back propagation (BP) strategy with the global searching ability of a genetic algorithm. A series of combinations of the dimensions and thermal mechanical properties of the solder and the corresponding singularity parameters at the failure interface are used to train the proposed GA-BP network. The results of the network, together with the established life prediction model, are used to predict the thermal fatigue lives of new chips. The comparison between the network results and thermal fatigue lives recorded in experiments shows that the GA-BP strategy is a successful prediction technique. View Full-Text
Keywords: thermal fatigue; microelectronic chip; singularity parameters; GA-BP; life prediction thermal fatigue; microelectronic chip; singularity parameters; GA-BP; life prediction
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Han, Z.; Huang, X. GA-BP in Thermal Fatigue Failure Prediction of Microelectronic Chips. Electronics 2019, 8, 542.

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