Chipmakers wrestle with EOS, ESD and other power-related issues as leading-edge chips are incorporated into industrial and automotive applications.

Chipmakers exploring nanosheet, nanoslab, nano-ring and hexagonal FETs.

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

Race begins to figure out what else can be done with data. But not all data is useful, and some of it is faulty.

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

Proponents tout freedom for computing architectures, but is the semiconductor ecosystem ready for open-source hardware?

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.

Part 2: Heterogeneity and architectures become focus as scaling benefits shrink; IP availability may be problematic.

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

Technology begins to twist in different directions and for different markets.

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

RTOS in space; NASA’s safety culture; FPGAs and neural nets; AI in nuclear plants.

Adding more dimensions creates more data, all of which needs to be processed using new architectural approaches.