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. 2023 Oct 31;14:6939. doi: 10.1038/s41467-023-42116-6

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — a Block diagram of photonic neural network integrated with our activation function devices. b, c Photocurrent and transmission versus bias voltage and input power of device 1 and device 2, respectively. Black dashed lines in the filled contours of photo current represent the current contours to generate activation functions. Black solid lines in the transmission surface are the corresponding transmission mapped from the photocurrent contours. The scattering plots are data points of the extracted activation functions. d Five activation functions with transmission and phase shift information deduced from device 1 and device 2 corresponding to different photocurrent contours.