Topology-Preserved Neural SDF Reconstruction for Simulation-Ready Meshing in Advanced Packaging

For integrated IC to integrated system, STCO extends DTCO from chip-level to system-level co-optimization; therefore, multiphysics modeling is the core-driven force for the STCO. However, mesh generation is essential for multiphysics simulations in advanced packaging. Traditionally, these meshes require tedious manual CAD repair; it sometimes can be even slower than the simulation itself. Alternatively, a learned signed distance function (SDF) can bypass CAD repair, while reconstruct meshes directly from the easily available point scans. Whereas, it often fails to capture critical internal features, such as vias and narrow gaps between stacked layers. To address this issue, we propose a new CAD-preserved SDF with the following two improvements: 1) We design a new topology-constrained algorithm before training by pre-computing constraints from the input data. This certificate is a checkable sufficient condition that, when it holds, preserves the topology of the learned field. 2) We introduce a discretization certificate, a second checkable condition under which the topology is still preserved after discretization. Meanwhile, we add a gradient-consistency regularizer to remove stripe artifacts from flat surfaces. To be specific, we verify that every measured through-path remains open on the TSV package. The meshes generated with these improvements are then used directly for steady-state thermal simulations in a commercial multiphysics solver, without manual geometry editing. Under a shared-field vertex-placement protocol, the resulting pipeline ranks first on all six metrics for the six chip-package models, each carrying its own free-space certificate pre-computed from its own scan; on three Thingi10K figurines, which have no internal corridors, the same placement rule leads on most metrics and isolates its geometric benefit from the topology claim.