Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.11141 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916652553076736 |
|---|---|
| author | Chen, Bei Xiong, Xiaowen Zhang, Renheng Dai, Yitang Yang, Jianyi Bai, Jinhua Li, Wei Zhu, Ninghua Li, Ming |
| author_facet | Chen, Bei Xiong, Xiaowen Zhang, Renheng Dai, Yitang Yang, Jianyi Bai, Jinhua Li, Wei Zhu, Ninghua Li, Ming |
| contents | Photonic neural networks have been considered as the promising candidates for next-generation neuromorphic computation, aiming to break both the power consumption wall and processing speed boundary of state-to-date digital computing architectures. Optics has shown its advantages in parallelism and linear manipulation. However, the lack of low-power and high-speed all-optical nonlinear activation neurons limits its revolution in large-scale photonic neural networks. Here we demonstrate an all-optical nonlinear activator (AONA) based on Fano-enhanced nonlinear optical effects in intra-cavity field, in which our device enables reconfigurability both in shape and type of the nonlinear functions (NFs) relying on the tuning biases on Fano interference and cavity buildup. Experimental results show that our AONA enables nonlinear optical computing with low-power continuous-wave light of 0.1 mW threshold, which can also support the overall processing speed of 13 GHz. The performances of the generated reconfigurable NFs are verified in the classification of handwritten digits and image recognition tasks, yielding the converged cost and enhanced accuracy compared to the linear-only networks. Our proposed device would pave the way for energy-efficient and accelerated all-optical intelligent processors with versatile functionalities and large-scale integration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Fully Reconfigurable All-Optical Integrated Nonlinear Activator Chen, Bei Xiong, Xiaowen Zhang, Renheng Dai, Yitang Yang, Jianyi Bai, Jinhua Li, Wei Zhu, Ninghua Li, Ming Optics Photonic neural networks have been considered as the promising candidates for next-generation neuromorphic computation, aiming to break both the power consumption wall and processing speed boundary of state-to-date digital computing architectures. Optics has shown its advantages in parallelism and linear manipulation. However, the lack of low-power and high-speed all-optical nonlinear activation neurons limits its revolution in large-scale photonic neural networks. Here we demonstrate an all-optical nonlinear activator (AONA) based on Fano-enhanced nonlinear optical effects in intra-cavity field, in which our device enables reconfigurability both in shape and type of the nonlinear functions (NFs) relying on the tuning biases on Fano interference and cavity buildup. Experimental results show that our AONA enables nonlinear optical computing with low-power continuous-wave light of 0.1 mW threshold, which can also support the overall processing speed of 13 GHz. The performances of the generated reconfigurable NFs are verified in the classification of handwritten digits and image recognition tasks, yielding the converged cost and enhanced accuracy compared to the linear-only networks. Our proposed device would pave the way for energy-efficient and accelerated all-optical intelligent processors with versatile functionalities and large-scale integration. |
| title | A Fully Reconfigurable All-Optical Integrated Nonlinear Activator |
| topic | Optics |
| url | https://arxiv.org/abs/2503.11141 |