Quantum computing applications; monitoring shared memory; simulating solid state batteries; trusting ML in automotive; ...

This eBook offers insights into what’s involved in photonics design, what’s changing (or at least what we know so far), and how those changes will affect semiconductor and electronic design in the ...

Researchers at Purdue University and the UCLA published a technical paper titled “Experimental Evidence for the Impact of ...

Moores Lab AI is betting that chip design AI will only work if it is built around deep semiconductor expertise, not generic ...

Verification data is not enough without verification context Most verification environments are good at producing outputs. They generate logs, waveforms, assertions, coverage metrics, pass/fail status ...

Aggressive prediction of $1T by 2030 was $700B too low. Here’s why.

Larger packages, finer routing, and embedded functions are pushing advanced substrates toward application-specific designs.

As AI chips move to stacked, chiplet-based architectures, EDA vendors are reworking mature tools for cross-domain analysis, faster exploration, and agentic AI assistance.

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

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