no code implementations • 30 Jun 2020 • Muhammed Veli, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Jingxi Li, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics.
no code implementations • 23 May 2020 • Deniz Mengu, Yifan Zhao, Nezih T. Yardimci, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN).
no code implementations • 15 May 2020 • Jingxi Li, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Xurong Li, Muhammed Veli, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction.
no code implementations • 14 Sep 2019 • Yi Luo, Deniz Mengu, Nezih T. Yardimci, Yair Rivenson, Muhammed Veli, Mona Jarrahi, Aydogan Ozcan
We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using deep learning.
no code implementations • 14 Apr 2018 • Xing Lin, Yair Rivenson, Nezih T. Yardimci, Muhammed Veli, Mona Jarrahi, Aydogan Ozcan
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively.