Dan Krantz, CIO at Keysight Technologies shares his Quantum Computing Predictions for 2024

Advanced Semiconductor Innovations on the Horizon

Connecting the digital and physical worlds will require more powerful digital processing and interfaces able to overcome increasingly complex signal physics. An array of advancements in semiconductor technologies will be essential to achieve this and overcome the associated challenges.

These issues include increasing data rates that need wider bandwidths, which dictate higher carrier frequencies, extending into the THz regime for wireless. The use of techniques such as extreme MIMO, adds more complexity and density, and networks with diverse topologies, such as the use of non-terrestrial (satellite) links, magnifies the challenge.

Innovations to address this will include combining commercial semiconductors, such as GPUs and FPGAs, with custom MMICs and ASICs, and these new solutions will deliver significant improvements in size, weight, performance, and power consumption.

Data converters enabling the capture and generation of signals at the widest bandwidths with unsurpassed signal fidelity will be needed. In addition, photonic solutions will be critical to extend the reach and capacity of data transmission technologies.

Seamless Software Solutions for Design and Test

Currently, workflows are a set of loosely connected tools; however, as the virtual and physical worlds merge, a unified design and test workflow where data is shared seamlessly via the cloud between simulation and measurement steps is required.

The information will be constantly analyzed to inform the behavior of simulation and measurement, eliminating any gaps in the workflow from concept to final test. The insights from the simulation will be fed into AI-driven tools that will elevate the speed and productivity of the design and test workflow. Digital twins will be used to tightly couple design and test so that only one actual build is needed.

6G Embraces AI for Network Optimization

6G will turn to AI for network optimization, which will create testing challenges. It will be vital to develop technologies able to test AI algorithms to ensure training data is free of bias and the models are effective and devoid of anomalous behavior.

Bridging the Simulation Gap with AI

Moving forward, AI technologies will underpin simulation models, ushering in a new era of more accurate, capable, and informative models. In addition, the intelligence will provide enhanced insights into measurement data, reduce errors, and help optimize the design and test workflow.