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 …

In recent years, with the further ministration of the semiconductor device in integrated
circuits, power consumption and data transmission bandwidth have become insurmountable …

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 …

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 …

Deep neural networks (DNNs) are reshaping the field of information processing. With the
exponential growth of these DNNs challenging existing computing hardware, optical neural …

Optical isolators are indispensable components of almost any optical system and are used
to protect a laser from unwanted reflections for phase-stable coherent operation. The …

Convolutional neural networks are an important category of deep learning, currently facing
the limitations of electrical frequency and memory access time in massive data processing …

Simultaneous implementation of photodetector and neuromorphic vision sensor (NVS) on a
single device faces a great challenge, due to the inherent speed discrepancy in their …

As deep neural networks (DNNs) revolutionize machine learning, energy consumption and
throughput are emerging as fundamental limitations of CMOS electronics. This has …

Integrated photonics, because of its intrinsic high speed, large bandwidth and unlimited
parallelism, is critical in the drive to ease the increasing data traffic. Its technological enabler …