Matrix computation, as a fundamental building block of information processing in science
and technology, contributes most of the computational overheads in modern signal …

The integration of functional nanomaterials and heterostructures with photonic architectures
has laid the foundation for important photonic and optoelectronic applications. The advent of …

Integrated quantum photonics uses classical integrated photonic technologies and devices
for quantum applications. As in classical photonics, chip-scale integration has become …

Integrated photonic neural networks provide a promising platform for energy-efficient, high-
throughput machine learning with extensive scientific and commercial applications. Photonic …

Silicon photonics is evolving from laboratory research to real-world applications with the
potential to transform many technologies, including optical neural networks and quantum …

Large-scale, highly integrated and low-power-consuming hardware is becoming
progressively more important for realizing optical neural networks (ONNs) capable of …

The growing maturity of nanofabrication has ushered massive sophisticated optical
structures available on a photonic chip. The integration of subwavelength-structured …

Deep neural networks with applications from computer vision to medical diagnosis,,,–are
commonly implemented using clock-based processors,,,,,,,–, in which computation speed is …

Silicon photonics is advancing rapidly in performance and capability with multiple fabrication
facilities and foundries having advanced passive and active devices, including modulators …

There has been a resurgence of interest in optical computing since the early 2010s, both in
academia and in industry, with much of the excitement centred around special-purpose …