Since its introduction, diffractive deep neural networks (D2NN) have been an emerging technology with many useful applications, such as 3D object recognition, saliency segmentation, and quantitative phase microscopy. However, fabricating D2NNs operating in the visible range has not been performed due to the complexities of fabricating nano-scale elements. Recent advancements such as Implosion Fabrication have made it possible to fabricate such networks using a discrete number of phase weights. We propose a quantization-aware training approach for D2NNs through modeling the quantization process in a differentiable manner using a sigmoid-based quantization function, facilitating the fabrication process. We also propose an efficient training schedule to guide the optimization process to converge to a better minima despite the limited number of quantization levels. Our method is simulated and validated for an all-optical quantitative phase microscopy task based on the phase D2NN.
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