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 …

Artificial intelligence tasks across numerous applications require accelerators for fast and
low-power execution. Optical computing systems may be able to meet these domain-specific …

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

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

Optical imaging is commonly used for both scientific and technological applications across
industry and academia. In image sensing, a measurement, such as of an object's position or …

Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …

New developments in hardware-based 'accelerators' range from electronic tensor cores and
memristor-based arrays to photonic implementations. The goal of these approaches is to …

Complex-valued neural networks have many advantages over their real-valued
counterparts. Conventional digital electronic computing platforms are incapable of executing …

Lithium niobate (LN), an outstanding and versatile material, has influenced our daily life for
decades—from enabling high-speed optical communications that form the backbone of the …

There is an ever-growing demand for artificial intelligence. Optical processors, which
compute with photons instead of electrons, can fundamentally accelerate the development …