| Autor: |
Gu Min, Dong Yibo, Yu Haoyi, Luan Haitao, Zhang Qiming |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Nanophotonics, Vol 12, Iss 5, Pp 827-832 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2192-8614 |
| DOI: |
10.1515/nanoph-2022-0437 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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