Emerging Integrated Photonic Neural Network Technologies for Artificial Intelligence: From Devices to Systems

Clicks: 1
ID: 317378
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #205 of 274 articles by views in national science review

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 274 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract The rapid expansion of artificial intelligence (AI) has created an explosive demand for computing power, while traditional von Neumann-based electronic computing faces the memory and interconnect bottlenecks. By migrating critical operations to the optical domain, photonic neural networks (PNNs) offer a promising solution to significantly reduce system latency and energy consumption. Driven by silicon (Si) photonics, on-chip PNNs achieve high-density integration on scalable platforms, promoting the evolution of optoelectronic integration systems. This paper presents a comprehensive review of emerging integrated PNN technology for AI, employing a cross-layer framework that spans from fundamental physical devices to system-level implementation. Based on the requirements of PNNs, this review analyzes key hardware devices and mainstream computing core architectures, evaluates integration strategies, and discusses advanced system packaging. Furthermore, the review provides a prospect on the field by identifying the remaining technical challenges and highlighting the transformative potential of PNNs as the infrastructure for next-generation AI.
Reference Key
openalex_W7164738436 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Changhao Han, Qipeng Yang, Jun Qin, M Wang, Zhaozheng Yi, Mingxiao Li, Y W Tian, 越曙晗, Xueling Wei, Wu Zj, Liuyan Han, D Zhang, J C Chen, Haowen Shu, Xingjun Wang, Wanhua Zheng, John E Bowers
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag335
URL
https://doi.org/10.1093/nsr/nwag335
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.