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"Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures", ACS Photonics 5, 4, 1365-1369 (2018). Author: Dianjing Liu, Yixuan Tan, Erfan Khoram and Zongfu Yu

This is the implementation of the tandem neural network introduced in the article. To reproduce the 2D deisgn result, run the code with mode='FNN' to train the forward network. Then run the code again with mode='tandem'.# inverse_design

Data set:

There are 600k instances in the training data and 5k instances in the test data. Both data files are N-by-18 matrices. Each row in the matrices is a training/test instance. The first 9 elements in each row are the input variables listed in Table 2 of the article: d1 x1 w1 d2 x2 w2 d3 x3 w3. The next 3 elements are the following 3 variables:

y1 = 400nm - x1 - w1,

y2 = 400nm - x2 - w2,

y3 = 400nm - x3 - w3.

Since they are duplicate with xi and wi (i=1,2,3), we do not use these 3 variables. The last 6 elements in each row corresponds to the labels. The design parameters in this task are (phi_R, phi_G, phi_B). The last 6 elements are sin(phi_R), sin(phi_G), sin(phi_B), cos(phi_R), cos(phi_G), cos(phi_B).

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