In this review we present some of the recent advances in the field of silicon nitride photonic
integrated circuits. The review focuses on the material deposition techniques currently …

Deep learning has led to unprecedented successes in solving some very difficult problems
in domains such as computer vision, natural language processing, and general pattern …

Deep learning has become a widespread tool in both science and industry. However,
continued progress is hampered by the rapid growth in energy costs of ever-larger deep …

The explosive growth of deep learning applications has triggered a new era in computing
hardware, targeting the efficient deployment of multiply-and-accumulate operations. In this …

Photonic neuromorphic computing is of particular interest due to its significant potential for
ultrahigh computing speed and energy efficiency. The advantage of photonic computing …

Optical implementations of neural networks (ONNs) herald the next-generation high-speed
and energy-efficient deep learning computing by harnessing the technical advantages of …

The number of photonic components integrated into the same circuit is approaching one
million, but so far, this has been without the large-scale integration of active components …

Analog photonic computing comprises a promising candidate for accelerating the linear
operations of deep neural networks (DNNs), since it provides ultrahigh bandwidth, low …

The explosive volume growth of deep-learning (DL) applications has triggered an era in
computing, with neuromorphic photonic platforms promising to merge ultra-high speed and …

Photonic computation has garnered huge attention due to its great potential to accelerate
artificial neural network tasks at much higher clock rate to digital electronic alternatives …