The present application relates to the technical field of integrated circuits, and discloses a method and device for 3D
integrated circuit thermal simulation and a storage medium. The existing method based on
machine learning ignores the shared structure of heat conduction and
diffusion type partial differential equations, resulting in the problem of needing to
train from zero, relying on massive data and poor generalization ability. The method comprises the following steps: obtaining a pre-trained basic model on a diversified
data set containing
diffusion type partial differential equations; fine-tuning the pre-trained model to adapt to the 3D
integrated circuit thermal simulation task, reinitializing the embedding layer and the
recovery layer to process specific input and output features, and keeping the
backbone network pre-training weight; applying different learning rates to different
layers in fine-tuning, using a lower learning rate for the
backbone network and a higher learning rate for the embedding layer and the
recovery layer; using a combined
loss function for optimization, including a global fidelity loss and a hotspot area loss emphasizing the prediction accuracy of high temperature areas; and using the fine-tuned model to predict the steady-state temperature field distribution of the 3D
integrated circuit.