Memristors in electronics have shown the potential for a range of applications, ranging from
circuit elements to neuromorphic computing. In recent years, the ability to vary the …

The exponential growth of information stored in data centers and computational power
required for various data-intensive applications, such as deep learning and AI, call for new …

The traditional ways of tuning a silicon photonic network are mainly based on the thermo-
optic effect or the free carrier dispersion. The drawbacks of these methods are the volatile …

Reconfigurable silicon photonic devices are widely used in numerous emerging fields such
as optical interconnects, photonic neural networks, quantum computing, and microwave …

Metasurfaces and nanoantennas are redefining what can be achieved in terms of optical
beam manipulation, as they provide a versatile design platform towards moulding the flow of …

Advances in deep learning research over the past decade have been enabled by an
increasingly unsustainable demand for compute power. This trend has dramatically …

The burgeoning of artificial intelligence has brought great convenience to people's lives as
large-scale computational models have emerged. Artificial intelligence-related applications …

We demonstrate a multilevel optical memristive switch based on a silicon Fabry–Perot
resonator. The resonator is constructed by a pair of waveguide Bragg gratings at the ends of …

Non-volatile storage memory is widely considered to be one of the most promising
candidates to replace dynamic random access memory and even static random access …

Silicon photonics (SiPh) technology has facilitated the deployment of integrated photonics
across different application domains, from ultra-fast communication in Datacom applications …