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16 March 2023 Towards fabricating visible wavelength D2NNs through multi-level quantization for quantitative phase imaging (Conference Presentation)
Hasindu Kariyawasam, Ramith Hettiarachchi, Quansan Yang, Udith Haputhanthri, Kithmini Herath, Chamira Edussooriya, Edward Boyden, Peter So, Dushan Wadduwage
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Proceedings Volume PC12389, Quantitative Phase Imaging IX; PC123890C (2023) https://doi.org/10.1117/12.2651937
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
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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Hasindu Kariyawasam, Ramith Hettiarachchi, Quansan Yang, Udith Haputhanthri, Kithmini Herath, Chamira Edussooriya, Edward Boyden, Peter So, and Dushan Wadduwage "Towards fabricating visible wavelength D2NNs through multi-level quantization for quantitative phase imaging (Conference Presentation)", Proc. SPIE PC12389, Quantitative Phase Imaging IX, PC123890C (16 March 2023); https://doi.org/10.1117/12.2651937
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KEYWORDS
Quantization
Phase imaging
Fabrication
Microscopy
Chemical elements
Machine learning
Neural networks
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