Sept. 26, 2025, Aug. 26, 2025 –
Artificial intelligence (AI) is transforming electronic design workflows—but not evenly. While some teams are racing ahead with AI-powered optimization, others remain stuck in the trenches, struggling to find the right version of a file or understand how a reused IP block is behaving in a new context.
That’s because many engineering teams are still battling fragmented design environments. Their data lives across multiple tools, formats, and directories. Version control is inconsistent. Metadata is unreliable. IP reuse is more trial-and-error than strategy. In this kind of chaos, AI-enhanced workflows can’t thrive. They stumble.
That’s why structured, contextualized, and accessible design data has become the new baseline. It’s not just about getting organized. It’s about unlocking the full potential of the semiconductor development process—from smarter reuse to predictive verification and generative layout.
From files to intelligence: The evolution of design data
At many semiconductor companies, “design data” is still treated like a digital filing cabinet. Files are stored, versioned (often manually), and eventually archived. But when data is structured—with contextual metadata, relationship tracking, dependency mapping, and naming standards—it becomes something more: a living, queryable model of a design ecosystem.
This evolution transforms data from a passive artifact into an active source of intelligence. It allows engineers, managers, and even AI models to navigate not just what was built, but how and why it evolved. It introduces history, intent, and trust into every downstream task.