Perspective on 3D vertically-integrated photonic neural networks based on VCSEL arrays

The rapid development of artificial intelligence has stimulated the interest in the novel designs of photonic neural networks. As three-dimensional (3D) neural networks, the diffractive neural networks (DNNs) relying on the diffractive phenomena of light, has demonstrated their superb performance in...

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Bibliographic Details
Published in:Nanophotonics (Berlin, Germany) Germany), 2023-03, Vol.12 (5), p.827-832
Main Authors: Gu, Min, Dong, Yibo, Yu, Haoyi, Luan, Haitao, Zhang, Qiming
Format: Article
Language:English
Subjects:
Arrays
Artificial intelligence
Bandwidths
Computer engineering
Design
diffractive neural networks (DNNs)
Energy consumption
Field programmable gate arrays
Input devices
integrated photonic neural network
Laser arrays
Lasers
Light
Light speed
Neural networks
Parallel processing
Photonics
Semiconductors
three-dimensional (3D) photonic chip
Vertical cavity surface emission lasers
vertical-cavity surface-emitting laser (VCSEL)
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Summary:The rapid development of artificial intelligence has stimulated the interest in the novel designs of photonic neural networks. As three-dimensional (3D) neural networks, the diffractive neural networks (DNNs) relying on the diffractive phenomena of light, has demonstrated their superb performance in the direct parallel processing of two-dimensional (2D) optical data at the speed of light. Despite the outstanding achievements, DNNs utilize centimeter-scale devices to generate the input data passively, making the miniaturization and on-chip integration of DNNs a challenging task. Here, we provide our perspective on utilizing addressable vertical-cavity surface-emitting laser (VCSEL) arrays as a promising data input device and integrated platform to achieve compact, active DNNs for next-generation on-chip vertical-stacked photonic neural networks. Based on the VCSEL array, micron-scale 3D photonic chip with a modulation bandwidth at tens of GHz can be available. The possible future directions and challenges of the 3D photonic chip are analyzed.
ISSN:2192-8614
2192-8606
2192-8614
DOI:10.1515/nanoph-2022-0437