Intelligent Photonics: A Disruptive Technology to Shape the Present and Redefine the Future

Artificial intelligence (AI) has taken breathtaking leaps forward in recent years, evolving into a strategic technology for pioneering the future. The growing demand for computing power—especially in demanding inference tasks, exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing systems. Advances in photonics technology have ignited interest in investigating photonic computing as a promising AI computing modality. Through the profound fusion of AI and photonics technologies, intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical applications. Deep learning, as a subset of AI, presents efficient avenues for optimizing photonic design, developing intelligent optical systems, and performing optical data processing and analysis. Employing AI in photonics can empower applications such as smartphone cameras, biomedical microscopy, and virtual and augmented reality displays. Conversely, leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy consumption. Applying photonics technology in AI computing is expected to have a transformative impact on diverse fields, including optical communications, automatic driving, and astronomical observation. Here, recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics, holography, and quantum photonics. This review also spotlights relevant applications and offers insights into challenges and prospects.

Authors

Publication Details

Journal
Engineering
Published
2024-09-06
DOI
https://doi.org/10.1016/j.eng.2024.08.016
Citations
28
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
9.73

Funders

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article

Intelligent Photonics: A Disruptive Technology to Shape the Present and Redefine the Future

Liangcai Cao, Danlin Xu, Yuchen Ma, Guofan Jin
28 citations
Engineering
Neural Networks and Reservoir Computing
9.73
article

Intelligent Photonics: A Disruptive Technology to Shape the Present and Redefine the Future

Liangcai Cao, Danlin Xu, Yuchen Ma, Guofan Jin
article en
28 citations

Abstract

Artificial intelligence (AI) has taken breathtaking leaps forward in recent years, evolving into a strategic technology for pioneering the future. The growing demand for computing power—especially in demanding inference tasks, exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing systems. Advances in photonics technology have ignited interest in investigating photonic computing as a promising AI computing modality. Through the profound fusion of AI and photonics technologies, intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical applications. Deep learning, as a subset of AI, presents efficient avenues for optimizing photonic design, developing intelligent optical systems, and performing optical data processing and analysis. Employing AI in photonics can empower applications such as smartphone cameras, biomedical microscopy, and virtual and augmented reality displays. Conversely, leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy consumption. Applying photonics technology in AI computing is expected to have a transformative impact on diverse fields, including optical communications, automatic driving, and astronomical observation. Here, recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics, holography, and quantum photonics. This review also spotlights relevant applications and offers insights into challenges and prospects.

EngineeringVol. 46
National Natural Science Foundation of China
Openalex Percentile: Top 2%
Neural Networks and Reservoir Computing
9.73
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