SCIENCE CHINA Physics, Mechanics & Astronomy, Volume 63 , Issue 8 : 284212(2020) https://doi.org/10.1007/s11433-020-1575-2

A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures

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  • ReceivedMar 3, 2020
  • AcceptedMay 7, 2020
  • PublishedJun 22, 2020
PACS numbers


Funded by

the National Natural Science Foundation of China(Grant,No.,ECCS-1916839)


This work was supported by the National Science Foundation (Grant No. ECCS-1916839).


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  • Figure 1

    (Color online) (a) The proposed deep learning model with self-supervised learning mechanism for both the forward prediction and inverse design of nanophotonic structures. The network architecture of encoder (b) and decoder (c). Conv stands for the convolutional block containing three convolution operations with kernel size of 1×1, 3×3 and 1×1, respectively, each followed by a batch-normalization layer. Pool denotes the pooling layer to halve the lateral dimension while U denotes the up-sampling layer to double the lateral dimension. Fc stands for the fully connected layer.

  • Figure 2

    (Color online) The total loss (a), reconstruction loss (b), and prediction loss (c) of four models (left), together with the loss evolution with training epochs (right).

  • Figure 3

    (Color online) Forward prediction of three samples, bowtie (a), ellipse (b) and split ring (c) from the test dataset. Top panels show the prediction without self-supervised learning (model U0) and bottom panels show the prediction with self-supervised learning (model U_dynamic).

  • Figure 4

    (Color online) Visualization of the latent space by reducing the dimension from 20 to 2 using t-SNE. The distribution of the nanophotonic structures from test dataset encoded by model U0 (a) and model U_dynamic (b), respectively.

  • Figure 5

    (Color online) Inverse design by the U_dynamic model. (a) Required spectra and the ground-truth design. (b)-(f) Retrieved designs and their corresponding reflection spectra.


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