Multi-wavelength diffractive neural network with the weighting method

PLUTO / PLUTO-2 Spatial Light Modulators
Deep Learning / Neuronal Network
Published on:
Authors: Jianan Feng, Hang Chen, Dahai Yang, Junbo Hao, Jie Lin, and Peng Jin
Abstract:

Recently, the diffractive deep neural network (D2NN) has demonstrated the advantages to achieve large-scale computational tasks in terms of high speed, low power consumption, parallelism, and scalability. A typical D2NN with cascaded diffractive elements is designed for monochromatic illumination. Here, we propose a framework to achieve the multi-wavelength D2NN (MW-D2NN) based on the method of weight coefficients. In training, each wavelength is assigned a specific weighting and their output planes construct the wavelength weighting loss function. The trained MW-D2NN can implement the classification of images of handwritten digits at multi-wavelength incident beams. The designed 3-layers MW-D2NN achieves a simulation classification accuracy of 83.3%. We designed a 1-layer MW-D2NN. The simulation and experiment classification accuracy are 71.4% and 67.5% at RGB wavelengths. Furthermore, the proposed MW-D2NN can be extended to intelligent machine vision systems for multi-wavelength and incoherent illumination.

Open Access

Publication: Optics Express
Issue/Year: Opt. Express 31, 33113-33122 (2023)
DOI: 10.1364/OE.499840
Link: https://doi.org/10.1364/OE.499840

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