New lithography tools will be required at 5nm, but pellicles, resists and uptime are still problematic.

Industrial IoT now dominates the market, but confusion is still rampant in all sectors.

OneSpin’s CEO looks at what’s needed in this computing space, which technologies are winning, and what the key metrics will be.

What comes after optical inspection isn’t clear, and that’s a problem.

Dynamic, thermal, packaging and electromagnetic effects grow, and so do the interactions between all of them.

There are multiple layers in which machine learning can help with the creation of semiconductors, but getting there is not as simple as for other application areas.

Machine learning needs techniques to prevent adversarial use, along with better data protection and management.

After two decades of experimentation, the semiconductor industry is scrambling to embrace this approach.

The mundane aspects of a system can make or break a solution, and interfaces often define what is possible.

This approach can make designs better and less expensive, but it will require a huge amount of work and more sharing of data.

If neural networking architectures are simpler than a CPU, why is it so difficult to create them?

Unique machine learning algorithms, diminished benefits from scaling, and a need for more granularity are creating a boom for accelerators.

Neuromorphic computing and machine learning dominate the leading edge of chip design this year.