Research on Performance Prediction and Intelligent Optimization Method of Polyamide-Based Composite Materials for Electronic Packaging Applications
Abstract
mechanical reliability of electronic packaging materials, the study considered polyamide composite materials and design a material performance prediction and intelligent optimization method for packaging. The study uses polyamide matrix type, insulating and thermally conductive
filler type, filler content, particle size distribution, surface modification information and processing parameters as inputs to derive a multisource feature expression of "formulation-structure-process-performance" and develop a multi performance-based adaptive optimization
model of polyamide compound materials (MPO-PAO) for four types of performance. The model has 4 modules: material feature encoding,
multi-task performance prediction, multiobjective constraint optimization, and feedback update. It is able to predict thermal conductivities,
dielectrics loss, tensile strength and coefficient of thermal expansion, and propose candidate formulations under electronic packaging constraints. Simulation results show that the MPO-PO model is at the best relative error of 3.5% for 4 performance classes, lower than the performance of the related models by BP neural network, support vector regression, random forest and XGBoost. After intelligent optimization,
thermal conductiveness of polyamite composite material is boosted from 0.31 W/(mK) to 1.34 W/(sK), tensile and dielectri-loss are around
88.6 MPa, the coefficient of temperature expansion is around 0.017, and the thermal expansion is about 43.8 ppm/K. The study showed that
our method can help in the screening of material formulations and provide a possible technical path to an intelligent design of polymite composite materials for electronic packaging.
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DOI: http://dx.doi.org/10.70711/frim.v4i5.9372
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