Hybrid neural network based multi-objective optimal design of hybrid pin-fin microchannel heatsink for integrated microsystems

Abstract

With the rapid advancement of 2.5D/3D heterogeneous integrated microsystems, the performance and fast intelligent design for thermal management are unprecedentedly required to address the electrical and mechanical reliability issues caused by thermal runaway. In this work, a hybrid neural network, featuring a small dataset requirement, is developed to accelerate the design of the hybrid pin-fin microchannel heatsink. Assisted by the trained surrogate model and the non-dominated sorting genetic algorithm, a powerful heatsink characterizing power-adaptive cooling capacity is designed. Firstly, a hybrid pin-fin microchannel heatsink is modeled and validated with the experimental data. The critical structural parameters correlated with the heat transfer and hydraulic performance are analyzed and identified through numerical simulation. A hybrid neural network serving as a surrogate model, is then developed to map the relationship between key structural parameters and the targeted performance indexes. The hybrid neural network achieves a prediction accuracy of at least 94.33 % and outperforms traditional networks, including DNN and CNN, in RMSE, MAE, and RE. It improves by 93.4 %, 89.5 %, and 87.8 % over DNN, and by 91.7 %, 93.0 %, and 91.9 % over CNN. The non-dominated sorting genetic algorithm is performed to explore the Pareto front where the intelligent design of power-adaptive pin-fin layout under uneven thermal profile is achieved. The performance indexes of the optimized heatsink are validated with that from the computational fluid dynamics. Compared with the original structure, it is found that enhancements of 5.58 %, 10.76 % and 45.73 % are achieved in the maximum temperature of high-power heat source, low-power heat source and the pressure drop of microchannel.