Volume 5 Issue 2, February 2022

The use of electric vehicles has increased substantially in recent years but the development of an appropriate charging infrastructure remains a challenge. Roads with dynamic wireless charging could provide an answer.

In-memory computing chips based on magnetoresistive random-access memory devices can provide energy-efficient hardware for machine learning tasks.

Multi-layer programmable metasurfaces can be used to construct diffractive neural networks in which radio waves are directly processed.

Suppression of superconductivity in metallic nanowires due to a gate voltage can be linked to the relaxation of high-energy electrons and not to the presence of electric fields at the superconductor surface.

By optimizing the doping and crystallization behaviour of solution-processed metal halide perovskite thin films, p-channel transistors with mobilities of 50 cm² V^–1 s^–1 and on/off ratios of 10⁸ can be fabricated.

Charge trapping mechanisms in molybdenum-disulfide-based transistors can be used to mimic the adaptive behaviour of human eyes, allowing vision sensors to be created with high dynamic range.

Multiple, small computational modules can be combined to create field-programmable gate-array-based stochastic neural network accelerators that are able to solve more complex problems than their individually trained parts.

Neuromorphic hardware designed to implement spiking neural networks for deep learning and artificial intelligence applications can also be used to solve non-cognitive computational tasks such as Monte Carlo methods.

Using a multi-layer metasurface array in which each meta-atom of the metasurface acts as an active artificial neuron, a programmable diffractive deep neural network can be created that directly processes electromagnetic waves in free space for wave sensing and wireless communications.