Siemens’ Matteo Depaola and Robin Bornoff examine how physics-based digital twins for electronics can enable a handful of carefully placed physical thermal sensors to provide the measurements needed to support many more virtual sensors throughout the system.

Synopsys’ Thomas Andersen explores how agentic AI systems change the unit of work from an individual prompt, command, or recommendation to a goal-directed workflow.

Cadence’s Max Zarco argues that traditional edge silicon can’t keep up with the demands of physical AI, which require multimodal workloads, stricter real-time constraints, and security anchored in hardware.

Keysight’s Richard Duvall finds that AI is fundamentally changing RF design by using advanced statistical models, deep learning networks, and automated reasoning to explore vast, high-dimensional RF design spaces rapidly.

Arm’s Julie Gaskin shares how neural workload profiling can help developers identify latency, expensive graph operations, and shader fallbacks in applications that use the ML extensions for Vulkan.

The ESD Alliance’s Julie Rogers and Paul Cohen chat with Jay Vleeschhouwer of Griffin Securities about whether EDA growth is sustainable, the impact of AI, 2027 revenue predictions.

Plus, check out the blogs featured in the latest Manufacturing, Packaging & Materials newsletter:

Mitsubishi Chemical Group’s Hideaki Okamoto looks at why expanding computational capabilities and deeper co-development are changing how materials are moving from lab to fab.

Amkor’s Chiung Lee outlines how thinner dies, taller stacks, and hybrid bonding are carrying NAND into the AI era, driven by data growth across consumer electronics, automotive, enterprise, and cloud.

Lam Research’s Swapnil Kailash More illustrates how small imbalances between stacked nanosheets can distort device behavior in ways that are difficult to predict from geometry alone.

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