  Static IR drop analysis is a fundamental and critical task in chip design
since the IR drop will significantly affect the design's functionality,
performance, and reliability. However, the process of IR drop analysis can be
time-consuming, potentially taking several hours. Furthermore, in the process
of fixing violations, it is frequently imperative to do IR drop analysis
iteratively, hence exacerbating the computational burden associated with the
analysis. Therefore, a fast and accurate IR drop prediction is paramount for
reducing the overall time invested in chip design. In this paper, we propose a
global and local attention-based Inception U-Net for static IR drop estimation.
Our U-Net incorporates components from the Transformer, CBAM, and Inception
architectures to enhance its feature capture capability at different scales and
improve the accuracy of predicted IR drop. Experimental results demonstrate
that our proposed algorithm can achieve the best results among the winning
teams of the ICCAD 2023 contest and the state-of-the-art algorithms.
