  Precise congestion prediction from a placement solution plays a crucial role
in circuit placement. This work proposes the lattice hypergraph (LH-graph), a
novel graph formulation for circuits, which preserves netlist data during the
whole learning process, and enables the congestion information propagated
geometrically and topologically. Based on the formulation, we further developed
a heterogeneous graph neural network architecture LHNN, jointing the routing
demand regression to support the congestion spot classification. LHNN
constantly achieves more than 35% improvements compared with U-nets and Pix2Pix
on the F1 score. We expect our work shall highlight essential procedures using
machine learning for congestion prediction.
