  As the size of circuit designs continues to grow rapidly, artificial
intelligence technologies are being extensively used in Electronic Design
Automation (EDA) to assist with circuit design. Placement and routing are the
most time-consuming parts of the physical design process, and how to quickly
evaluate the placement has become a hot research topic. Prior works either
transformed circuit designs into images using hand-crafted methods and then
used Convolutional Neural Networks (CNN) to extract features, which are limited
by the quality of the hand-crafted methods and could not achieve end-to-end
training, or treated the circuit design as a graph structure and used Graph
Neural Networks (GNN) to extract features, which require time-consuming
preprocessing. In our work, we propose a novel perspective for circuit design
by treating circuit components as point clouds and using Transformer-based
point cloud perception methods to extract features from the circuit. This
approach enables direct feature extraction from raw data without any
preprocessing, allows for end-to-end training, and results in high performance.
Experimental results show that our method achieves state-of-the-art performance
in congestion prediction tasks on both the CircuitNet and ISPD2015 datasets, as
well as in design rule check (DRC) violation prediction tasks on the CircuitNet
dataset. Our method establishes a bridge between the relatively mature point
cloud perception methods and the fast-developing EDA algorithms, enabling us to
leverage more collective intelligence to solve this task. To facilitate the
research of open EDA design, source codes and pre-trained models are released
at https://github.com/hustvl/circuitformer.
