  Thermal issue is a major concern in 3D integrated circuit (IC) design.
Thermal optimization of 3D IC often requires massive expensive PDE simulations.
Neural network-based thermal prediction models can perform real-time prediction
for many unseen new designs. However, existing works either solve 2D
temperature fields only or do not generalize well to new designs with unseen
design configurations (e.g., heat sources and boundary conditions). In this
paper, for the first time, we propose DeepOHeat, a physics-aware operator
learning framework to predict the temperature field of a family of heat
equations with multiple parametric or non-parametric design configurations.
This framework learns a functional map from the function space of multiple key
PDE configurations (e.g., boundary conditions, power maps, heat transfer
coefficients) to the function space of the corresponding solution (i.e.,
temperature fields), enabling fast thermal analysis and optimization by
changing key design configurations (rather than just some parameters). We test
DeepOHeat on some industrial design cases and compare it against Celsius 3D
from Cadence Design Systems. Our results show that, for the unseen testing
cases, a well-trained DeepOHeat can produce accurate results with $1000\times$
to $300000\times$ speedup.
