Passive All-Optical Nonlinear Neuron Activation via PPLN Nanophotonic Waveguides
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908869340430336 |
|---|---|
| author | Fu, Wujie Shi, Xiaodong Mohanraj, Sakthi Sanjeev Shi, Lei Gao, Yuan Wang, Zexian Wang, Jianing Chen, Xu Qi, Luo Aashna, Pragati Chen, Guanyu Zhu, Di Danner, Aaron |
| author_facet | Fu, Wujie Shi, Xiaodong Mohanraj, Sakthi Sanjeev Shi, Lei Gao, Yuan Wang, Zexian Wang, Jianing Chen, Xu Qi, Luo Aashna, Pragati Chen, Guanyu Zhu, Di Danner, Aaron |
| contents | Artificial intelligence (AI) is transforming modern life, yet the growing scale of AI applications places mounting demands on computational resources, raising sustainability concerns. Photonic integrated circuits (PICs) offer a promising alternative, enabling massive parallelism, low latency, and reduced electrical overhead, particularly excelling in high-throughput linear operations. However, passive and fully optical nonlinear activation functions with equally superb performance remain rare, posing a critical bottleneck in realizing all-optical neural networks in PICs. Here, we demonstrate a compact and integrated all-optical nonlinear activation method, experimentally realized through strong second-order optical nonlinearities in periodically poled lithium niobate (PPLN) nanophotonic waveguides, achieving 80% absolute conversion efficiency. This activation exhibits a sigmoid-like, wavelength-selective response with femtosecond-scale dynamics and light-speed processing, operating passively without external control and auxiliary signals. We validate its feasibility for neural inference by cascading the PPLN-driven activations with a linear silicon PIC, demonstrating all-optical nonlinear neuron expressivity. Moreover, combining the measured nonlinearity with linear operations calculated by the PIC, we show that PPLN-activated multi-layer optical neural networks can achieve performance on par with digital implementations in real-world tasks, including airfoil regression and medical image classification. These results pave the way toward scalable, high-speed, and fully integrated all-optical neural networks for next-generation photonic AI hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18145 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Passive All-Optical Nonlinear Neuron Activation via PPLN Nanophotonic Waveguides Fu, Wujie Shi, Xiaodong Mohanraj, Sakthi Sanjeev Shi, Lei Gao, Yuan Wang, Zexian Wang, Jianing Chen, Xu Qi, Luo Aashna, Pragati Chen, Guanyu Zhu, Di Danner, Aaron Optics Applied Physics Artificial intelligence (AI) is transforming modern life, yet the growing scale of AI applications places mounting demands on computational resources, raising sustainability concerns. Photonic integrated circuits (PICs) offer a promising alternative, enabling massive parallelism, low latency, and reduced electrical overhead, particularly excelling in high-throughput linear operations. However, passive and fully optical nonlinear activation functions with equally superb performance remain rare, posing a critical bottleneck in realizing all-optical neural networks in PICs. Here, we demonstrate a compact and integrated all-optical nonlinear activation method, experimentally realized through strong second-order optical nonlinearities in periodically poled lithium niobate (PPLN) nanophotonic waveguides, achieving 80% absolute conversion efficiency. This activation exhibits a sigmoid-like, wavelength-selective response with femtosecond-scale dynamics and light-speed processing, operating passively without external control and auxiliary signals. We validate its feasibility for neural inference by cascading the PPLN-driven activations with a linear silicon PIC, demonstrating all-optical nonlinear neuron expressivity. Moreover, combining the measured nonlinearity with linear operations calculated by the PIC, we show that PPLN-activated multi-layer optical neural networks can achieve performance on par with digital implementations in real-world tasks, including airfoil regression and medical image classification. These results pave the way toward scalable, high-speed, and fully integrated all-optical neural networks for next-generation photonic AI hardware. |
| title | Passive All-Optical Nonlinear Neuron Activation via PPLN Nanophotonic Waveguides |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2504.18145 |