Research in photonic computing has flourished due to the proliferation of optoelectronic
components on photonic integration platforms. Photonic integrated circuits have enabled …

Multimode silicon photonics is attracting more and more attention because the introduction
of higher-order modes makes it possible to increase the channel number for data …

Predicting physical response of an artificially structured material is of particular interest for
scientific and engineering applications. Here we use deep learning to predict optical …

Recent progress in artificial intelligence is largely attributed to the rapid development of
machine learning, especially in the algorithm and neural network models. However, it is 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 incorporation of augmentative functionalities into a single synaptic device is greatly
desired to enhance the performance of neuromorphic computing, which has brain-like high …

Superconducting optoelectronic hardware could be used to create large-scale and
computationally powerful artificial spiking neural networks. The approach combines …

Neuromorphic computing is a broad field that uses biological inspiration to address
computing design. It is being pursued in many hardware technologies, both novel and …

Emerging neuromorphic hardware promises to solve certain problems faster and with higher
energy efficiency than traditional computing by using physical processes that take place at …

Abstract 2D metal carbides and nitrides (MXene) are promising material platforms for on‐
chip neural networks owing to their nonlinear saturable absorption effect. The localized …