Low operating power, low activation threshold, and diverse all-optical nonlinear activation based on a PPLN resonator.

Authors

Lei Shi, Yuxuan Cheng, Ruozhou Li, Wujie Fu, Yuze Liu, Minghao Shang, Yuhui Yin, Xiaoyang Zhang, Aaron J Danner, Tong Zhang

Published in

Optics letters. Volume 51. Issue 17. Pages 4932-4935. Sep 01, 2026.

Abstract

Optical neural networks (ONNs) are considered next-generation physical implementations of artificial neural networks, yet their capabilities are constrained by the availability of nonlinear activation functions (NAFs). Current NAF implementations suffer from limited integrability, complex device architectures, and high latency. Here, we theoretically explore diverse all-optical nonlinear activations based on a periodically poled lithium niobate (PPLN) resonator. The device supports passive propagation and features an ultralow activation threshold (0.1 mW) and low operating power (below 6 mW). The underlying physical dynamics are mapped to nonlinear activation, enabling high classification accuracy on the Moons, Iris, and MNIST datasets. These results highlight the potential of this approach for ONNs. We believe this work will facilitate future large-scale photonic intelligent processors with enhanced functionality and simplified architectures.

PMID:
42679283
Bibliographic data and abstract were imported from PubMed on 02 Sep 2026.

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