Blog Review: Aug. 15

China’s semi growth; when formal gets stuck; 5G & AI; electroplating.

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Cadence’s Paul McLellan checks out what’s driving the growth of China’s semiconductor industry plus the state of fab construction, from a CAPSA presentation by SEMI’s Lung Chu.

Mentor’s Joe Hupcey III has some tips for how to handle inconclusive results in formal verification, starting with how to identify where the analysis got stuck.

Synopsys’ Taylor Armerding listens in on a presentation at Black Hat that explains how not to respond when notified of a security vulnerability – especially if you’re a medical device maker.

Lam Research’s Shelly Miyasato provides a primer on the electroplating process and how it’s used in the chipmaking process to produce low resistivity, void-free, high reliability structures.

GlobalFoundries’ Peter A. Rabbeni argues that AI techniques are essential to the successful rollout of 5G, where it can be used to optimize millimeter wave networks through techniques such as adaptive beamforming.

Arm’s Sylwester Bala traces the development of augmented reality from the earliest systems of the ’60s and Boeing’s use in aircraft assembly to the current mobile-focused wave.

Digital twins are a frequently-discussed topic for factory machinery, but what about for people? Ansys’ Thierry Marchal takes a look at ways they’re being implemented in healthcare settings.

SEMI’s Jay Chittooran opines on the impact on the semiconductor industry of the second round of tariffs in the U.S.-China trade war.

A Rambus writer shares a quick introduction to white box cryptography and how it resists reverse engineering threats.

Nvidia’s Isha Salian points to yet another use for neural networks: identifying the multitude of craters marking the moon’s surface.

And don’t miss the highlighted blogs from last week’s Low Power-High Performance newsletter:

Editor in Chief Ed Sperling predicts scaling will take on a whole different look, and not just from feature shrinks.

Cadence’s Rob Knoth explains why it’s critical to make power a key vector for design convergence.

Rambus’ Liji Gopalakrishnan contends that emerging applications drive the need for high memory capacity.

ANSYS’ Annapoorna Krishnaswamy observes that increased power density is ratcheting up power and thermal concerns, which need to be considered as early as possible.

Synopsys’ Ron Lowman argues that deep learning applications will require specialized IP in the form of new processing and memory architectures.

Helic’s Magdy Abadir examines how designers can detect increasing sensitivity to electromagnetic coupling.

Fraunhofer’s Olaf Enge-Rosenblatt looks at the combination of data analytics and process knowledge to predict machine failures.

Mentor’s Tom Fitzpatrick notes the fully updated and free how-to guide for UVM is now available.

Arm’s Brian Fuller finds more powerful edge devices are making AI applications like social robots feasible.



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