TCAD Device Simulation With Graph Neural Network
IEEE Electron Device Letters(2023)
摘要
There is an increasing number of studies to accelerate the TCAD simulation with deep learning models. Such studies rely on performing a procedure that interpolates an unstructured mesh into a structured mesh. This procedure, however, incurs intrinsic errors and redundant computation. To avoid this unnecessary procedure, this letter proposes a new method that can treat unstructured mesh itself to mimic TCAD device simulation. The method is to convert the unstructured mesh into a graph and then, directly applies a novel graph neural network (MHAT-GNN). In 45nm process, the proposed method outperforms pre-existing methods in terms of accuracy and efficiency.
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关键词
TCAD simulation,mesh,graph neural network (GNN),multi-hops,affine transformation
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