Highly integrated electrical components produce intensive heat while in use, which will seriously impact their performance if not properly designed. In this study, an end-to-end heat dissipation structure topology optimization prediction framework considering physical mechanisms was established by using the convolutional neural network (CNN) and the moving morphable components (MMC) method. Aiming at the sparsity of physical field matrix caused by the initial component distribution in MMC method, a CNN model was established taking the temperature gradient information of both homogeneous material and initial component layout as input. Compared with other seven input forms, the CNN model in this study considers both the initial component layout and the physical field information of the structure, which can predict the topology configuration of heat dissipation structure more accurately. In addition, an improved penalty mean square error (PMSE) function was proposed by introducing a penalty factor, which improved the prediction ability of the CNN model on the structural boundary and ensured more accurate and efficient structural heat dissipation performance. Several 2D and 3D numerical examples verified the effectiveness of the proposed framework and the dual temperature gradient input model. The overall framework provides a new method for the innovative and efficient heat dissipation structure topology optimization in packaging structure of electronic equipment.

高度集成的电气元件在使用时会产生大量热量，如果设计不当，将严重影响其性能。本研究利用卷积神经网络（CNN）和移动可变形组件（MMC）方法建立了考虑物理机制的端到端散热结构拓扑优化预测框架。针对MMC方法中初始元件分布导致的物理场矩阵稀疏的问题，以均质材料和初始元件布局的温度梯度信息为输入，建立了CNN模型。与其他七种输入形式相比，本研究的CNN模型既考虑了初始组件布局，又考虑了结构的物理场信息，可以更准确地预测散热结构的拓扑结构。此外，通过引入惩罚因子，提出了改进的惩罚均方误差（PMSE）函数，提高了CNN模型对结构边界的预测能力，保证了结构散热性能更加准确高效。几个2D和3D数值例子验证了所提出的框架和双温度梯度输入模型的有效性。该总体框架为电子设备封装结构中创新高效的散热结构拓扑优化提供了一种新方法。提高了CNN模型对结构边界的预测能力，保证了结构散热性能更加准确高效。几个2D和3D数值例子验证了所提出的框架和双温度梯度输入模型的有效性。该总体框架为电子设备封装结构中创新高效的散热结构拓扑优化提供了一种新方法。提高了CNN模型对结构边界的预测能力，保证了结构散热性能更加准确高效。几个2D和3D数值例子验证了所提出的框架和双温度梯度输入模型的有效性。该总体框架为电子设备封装结构中创新高效的散热结构拓扑优化提供了一种新方法。