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 \emph{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 \url{https://anonymous.4open.science/r/iPCL-R-0778/}.