
Developed a bounding-box based via reduction feature for the ansys/pyedb repository, focusing on backend and EDA tool development using Python. The solution decreases the number of vias within a user-defined bounding box by sampling grid points and removing vias outside the sampled area, optimizing data scalability and reducing storage requirements for routing analysis. The implementation addressed edge cases such as empty bounding boxes and insufficient vias for sampling, ensuring robust and reliable operation. This work improved the efficiency of downstream design rule checks and connectivity analyses by streamlining via data, aligning with ongoing efforts to optimize spatial queries and database footprint.
December 2024 monthly summary for ansys/pyedb: Delivered a bounding-box based via reduction feature that decreases the number of vias inside a user-defined bounding box by sampling grid points and removing vias outside the sampled grid. The implementation includes edge-case handling for scenarios with no vias and insufficient vias for sampling, and reports successful reduction. This enhancement improves data scalability, reduces storage and processing overhead for routing analyses, and speeds up subsequent DRC and connectivity checks by focusing analysis on a representative subset of vias within the area of interest. The work aligns with ongoing efforts to optimize spatial queries and data footprint in the via database.
December 2024 monthly summary for ansys/pyedb: Delivered a bounding-box based via reduction feature that decreases the number of vias inside a user-defined bounding box by sampling grid points and removing vias outside the sampled grid. The implementation includes edge-case handling for scenarios with no vias and insufficient vias for sampling, and reports successful reduction. This enhancement improves data scalability, reduces storage and processing overhead for routing analyses, and speeds up subsequent DRC and connectivity checks by focusing analysis on a representative subset of vias within the area of interest. The work aligns with ongoing efforts to optimize spatial queries and data footprint in the via database.

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