AI may accelerate semiconductor design, but users still need formal proof, semantic continuity, and auditable workflows to trust automation.

Autonomous inspection robots detect overheating, leaks, and equipment anomalies in fabs before they cause failures, while keeping technicians out of hazardous environments. Connecting equipment ...

First-silicon success falls; engineering capacity; minimum clock period; optimizing PyTorch; counterfeit electronics.

A mixture of expert agentic AI systems can focus on their tasks with or without a commanding general, but challenges remain ...

Researchers at the University of Wisconsin–Madison and Marist University published a technical paper titled “Demystifying ...

Increasingly complex chip designs require more test data than those developed at older nodes and on single planar dies. The ...

Most conversations about AI start and end with compute. But every training run and every inference query is ultimately a data problem, and data must live somewhere. Memory, and increasingly, the ...

Researchers at UCLA published a technical paper titled “Can Agents Design Better Chips with a Higher Level Abstraction?” ...

Why design teams must organize before they optimize and how to utilize a purpose-built foundation for AI-ready data management across the chip design lifecycle.

Design data management, traceability, and revision control are critical for multi-chiplet heterogeneous integration.

As AI systems scale, advanced power delivery innovations are becoming critical to performance, efficiency and reliability.

Even though the benefits are accepted, a combined hardware/software development flow is hampered by a lot of challenges.