Reconfigurable nonlinear optical computing device for retina-inspired computing
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913683912785920 |
|---|---|
| author | Hua, Xiayang Zheng, Jiyuan Zhao, Peiyuan Ren, Hualong Zeng, Xiangwei Hao, Zhibiao Sun, Changzheng Xiong, Bing Han, Yanjun Wang, Jian Li, Hongtao Gan, Lin Luo, Yi Wang, Lai |
| author_facet | Hua, Xiayang Zheng, Jiyuan Zhao, Peiyuan Ren, Hualong Zeng, Xiangwei Hao, Zhibiao Sun, Changzheng Xiong, Bing Han, Yanjun Wang, Jian Li, Hongtao Gan, Lin Luo, Yi Wang, Lai |
| contents | Optical neural networks are at the forefront of computational innovation, utilizing photons as the primary carriers of information and employing optical components for computation. However, the fundamental nonlinear optical device in the neural networks is barely satisfied because of its high energy threshold and poor reconfigurability. This paper proposes and demonstrates an optical sigmoid-type nonlinear computation mode of Vertical-Cavity Surface-Emitting Lasers (VCSELs) biased beneath the threshold. The device is programmable by simply adjusting the injection current. The device exhibits sigmoid-type nonlinear performance at a low input optical power ranging from merely 3-250 μW. The tuning sensitivity of the device to the programming current density can be as large as 15 μW*mm2/mA. Deep neural network architecture based on such device has been proposed and demonstrated by simulation on recognizing hand-writing digital dataset, and a 97.3% accuracy has been achieved. A step further, the nonlinear reconfigurability is found to be highly useful to enhance the adaptability of the networks, which is demonstrated by significantly improving the recognition accuracy by 41.76%, 19.2%, and 25.89% of low-contrast hand-writing digital images under high exposure, low exposure, and high random noise respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reconfigurable nonlinear optical computing device for retina-inspired computing Hua, Xiayang Zheng, Jiyuan Zhao, Peiyuan Ren, Hualong Zeng, Xiangwei Hao, Zhibiao Sun, Changzheng Xiong, Bing Han, Yanjun Wang, Jian Li, Hongtao Gan, Lin Luo, Yi Wang, Lai Optics Applied Physics Optical neural networks are at the forefront of computational innovation, utilizing photons as the primary carriers of information and employing optical components for computation. However, the fundamental nonlinear optical device in the neural networks is barely satisfied because of its high energy threshold and poor reconfigurability. This paper proposes and demonstrates an optical sigmoid-type nonlinear computation mode of Vertical-Cavity Surface-Emitting Lasers (VCSELs) biased beneath the threshold. The device is programmable by simply adjusting the injection current. The device exhibits sigmoid-type nonlinear performance at a low input optical power ranging from merely 3-250 μW. The tuning sensitivity of the device to the programming current density can be as large as 15 μW*mm2/mA. Deep neural network architecture based on such device has been proposed and demonstrated by simulation on recognizing hand-writing digital dataset, and a 97.3% accuracy has been achieved. A step further, the nonlinear reconfigurability is found to be highly useful to enhance the adaptability of the networks, which is demonstrated by significantly improving the recognition accuracy by 41.76%, 19.2%, and 25.89% of low-contrast hand-writing digital images under high exposure, low exposure, and high random noise respectively. |
| title | Reconfigurable nonlinear optical computing device for retina-inspired computing |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2502.05410 |