Presentation
CADSTROM: Beyond DRC: How AI-Driven Component Modeling Is Closing the Verification Gap Between Schematic and Silicon
DescriptionDesign verification has made enormous strides at the chip level, yet a stubborn class of errors continues to plague teams working at the board and system level — voltage mismatches, misconfigured interfaces, overlooked datasheet constraints, and derating violations that standard DRC tools weren't built to catch. These aren't exotic corner cases. They're fundamental checks that experienced engineers know matter but that are tedious to perform manually and easy to miss under schedule pressure. The result: unnecessary respins, late-cycle fire drills, and eroded confidence in design closure.
This session explores how AI-assisted validation can bridge that gap by automatically constructing detailed behavioral models for every component in a design — grounded in actual datasheet specifications, not heuristic rules — and running deterministic checks on interface compatibility, power sequencing, thermal derating, and bus configuration as the schematic evolves. Unlike LLM-based "copilot" approaches, this methodology produces findings that are verifiable, traceable, and citable back to source documentation, while keeping proprietary design IP fully protected.
We'll walk through anonymized case studies from design consultancies, medical device OEMs, and industrial electronics teams where automated validation uncovered previously unknown, fabrication-blocking issues early enough to resolve with a schematic edit instead of a board respin. Attendees will leave with a concrete framework for where AI-assisted verification fits alongside existing EDA workflows — complementing, not replacing, the tools and judgment they already rely on.
This session explores how AI-assisted validation can bridge that gap by automatically constructing detailed behavioral models for every component in a design — grounded in actual datasheet specifications, not heuristic rules — and running deterministic checks on interface compatibility, power sequencing, thermal derating, and bus configuration as the schematic evolves. Unlike LLM-based "copilot" approaches, this methodology produces findings that are verifiable, traceable, and citable back to source documentation, while keeping proprietary design IP fully protected.
We'll walk through anonymized case studies from design consultancies, medical device OEMs, and industrial electronics teams where automated validation uncovered previously unknown, fabrication-blocking issues early enough to resolve with a schematic edit instead of a board respin. Attendees will leave with a concrete framework for where AI-assisted verification fits alongside existing EDA workflows — complementing, not replacing, the tools and judgment they already rely on.
Presenter
Event Type
Exhibitor Forum
TimeMonday, July 275:15pm - 5:45pm PDT
LocationExhibitor Forum, Exhibit Floor