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Abstract Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware. Photonics offers a promising solution by leveraging the unique properties of light. However, conventional neural network architectures, which typically require dense programmable connections, pose several practical challenges for photonic realizations. To overcome these limitations, we propose and experimentally demonstrate Photonic Neural Cellular Automata (PNCA) for photonic deep learning with sparse connectivity. PNCA harnesses the speed and interconnectivity of photonics, as well as the self-organizing nature of cellular automata through local interactions to achieve robust, reliable, and efficient processing. We utilize linear light interference and parametric nonlinear optics for all-optical computations in a time-multiplexed photonic network to experimentally perform self-organized image classification. We demonstrate binary (two-class) classification of images using as few as 3 programmable photonic parameters, achieving high experimental accuracy with the ability to also recognize out-of-distribution data. The proposed PNCA approach can be adapted to a wide range of existing photonic hardware and provides a compelling alternative to conventional photonic neural networks by maximizing the advantages of light-based computing whilst mitigating their practical challenges. Our results showcase the potential of PNCA in advancing photonic deep learning and highlights a path for next-generation photonic computers.
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This content will become publicly available on January 29, 2026
Interfacing Nanophotonics with Deep Neural Networks: AI for Photonic Design and Photonic Implementation of AI
Abstract Recent remarkable progress in artificial intelligence (AI) has garnered tremendous attention from researchers, industry leaders, and the general public, who are increasingly aware of AI's growing impact on everyday life. The advancements of AI and deep learning have also significantly influenced the field of nanophotonics. On the one hand, deep learning facilitates data‐driven strategies for optimizing and solving forward and inverse problems of nanophotonic devices. On the other hand, photonic devices offer promising optical platforms for implementing deep neural networks. This review explores both AI for photonic design and photonic implementation of AI. Various deep learning models and their roles in the design of photonic devices are introduced, analyzing the strengths and challenges of these data‐driven methodologies from the perspective of computational cost. Additionally, the potential of optical hardware accelerators for neural networks is discussed by presenting a variety of photonic devices capable of performing linear and nonlinear operations, essential building blocks of neural networks. It is believed that the bidirectional interactions between nanophotonics and AI will drive the coevolution of these two research fields.
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- Award ID(s):
- 2323909
- PAR ID:
- 10593724
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Laser & Photonics Reviews
- Volume:
- 19
- Issue:
- 8
- ISSN:
- 1863-8880
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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