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The recent advent of diffractive deep neural networks or D2NNs has opened new avenues for the design and optimization of multi-functional optical materials; despite the effectiveness of the D2NN approach, there is a need for making these networks as well as the design algorithms more general and computationally efficient. The work demonstrated in this paper brings significant improvements to both these areas by introducing an algorithm that performs inverse design on fully nonlinear diffractive deep neural network - assisted by an adjoin
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