ChiP-STAR: Spatial-Topological Attention for Pre-trained Generative Chip Routing

Routing is foundational in VLSI physical design, directly shaping timing, power, and area. Recent pre-trained Seq2Seq models recast routing as autoregressive generation over DFS-serialized trees, but inherit a text backbone blind to the physical space their tokens describe: DFS decouples sequence position from physical coordinate, breaking spatial reasoning at branching points, in load selection, and along the autoregressive trajectory. We name these failures the Three Spatial Blindnesses and propose ChiP-STAR, which augments a T5Gemma backbone with three lightweight modules: a dual-frame rotary encoding coupling sequence order with 3D proximity, a Fourier-factorized attention prior with memory linear in sequence length, and a cumulative coordinate-noise schedule closing the train–inference gap, together adding under $0.5\%$ parameters. We rebuild the AiEDA corpus into the largest geometry-annotated routing dataset to date ($6.4$M nets, $1.8$B tokens, $556$ GB). ChiP-STAR-Large surpasses iPCL-Large by $5\%$ on Leaf IoU and Connectivity, and commercial ECO signoff yields $3\%$ shorter wirelength and $11\%$ fewer vias. Our code is publicly available at https://anonymous.4open.science/r/iPCL-R-0778/.