  Circuit representation learning has shown promising results in advancing the
field of Electronic Design Automation (EDA). Existing models, such as DeepGate
Family, primarily utilize Graph Neural Networks (GNNs) to encode circuit
netlists into gate-level embeddings. However, the scalability of GNN-based
models is fundamentally constrained by architectural limitations, impacting
their ability to generalize across diverse and complex circuit designs. To
address these challenges, we introduce DeepGate3, an enhanced architecture that
integrates Transformer modules following the initial GNN processing. This novel
architecture not only retains the robust gate-level representation capabilities
of its predecessor, DeepGate2, but also enhances them with the ability to model
subcircuits through a novel pooling transformer mechanism. DeepGate3 is further
refined with multiple innovative supervision tasks, significantly enhancing its
learning process and enabling superior representation of both gate-level and
subcircuit structures. Our experiments demonstrate marked improvements in
scalability and generalizability over traditional GNN-based approaches,
establishing a significant step forward in circuit representation learning
technology.
