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All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces
Onur Kulce, Deniz Mengu, Yair Rivenson +1
We report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (N_i) and output (N_o), where N_i and N_o rep…
Neural network-based on-chip spectroscopy using a scalable plasmonic encoder
Calvin Brown, Artem Goncharov, Zachary Ballard +5
Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral re…
Scale-, shift- and rotation-invariant diffractive optical networks
Deniz Mengu, Yair Rivenson, Aydogan Ozcan
Recent research efforts in optical computing have gravitated towards developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/phot…
Recurrent neural network-based volumetric fluorescence microscopy
Luzhe Huang, Yilin Luo, Yair Rivenson +1
Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-…
Deep learning-based holographic polarization microscopy
Tairan Liu, Kevin de Haan, Bijie Bai +8
Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically…
Terahertz Pulse Shaping Using Diffractive Surfaces
Muhammed Veli, Deniz Mengu, Nezih T. Yardimci +5
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive…