A scalable LPDDR-based memory platform optimized for edge AI inferencing.

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

Expanding computational capabilities and deeper co-development are changing how materials move from lab to fab.

Warpage is becoming a bigger constraint as package, interposer, and panel sizes grow. Negative thermal expansion (NTE) ...

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

Through better manufacturing screening, DFT for systems and fleet monitors, this major scourge on data center hardware is gradually being tamed.

Cost per token is causing EDA design budgets to balloon; what comes next isn’t entirely clear yet. Discussions have shifted ...

How advanced NoC architectures and coherent subsystem IP can address the industry's next-gen scalability, safety, and ...

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 ...

The only solution is an electronic design automation (EDA) toolset that can accurately predict chip behavior before any wafers are run, and even before the fab is ready. This enables the very first ...

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

Just because a chip is deemed good doesn’t mean it will work as expected.