Tag: Optical Computing

Electromagnetics

DNNsolver introduces a deep learning-based approach for modeling polarization-sensitive diffractive neural networks, addressing the limitation of thin element approximation in capturing intra-layer electromagnetic coupling for improved accuracy and efficiency in high-density optical computing.

Electromagnetics

DNNsolver proposes a deep learning approach for accurate and efficient modeling of polarization-sensitive diffractive neural networks, addressing limitations of thin element approximation in capturing intra-layer electromagnetic coupling.

Electromagnetics

Demonstrates a large-scale metasurface-based reflection cavity five-layer diffractive neural network achieving angle-multiplexed multi-task processing, addressing scalability limitations in all-optical computing.

Electromagnetics

DNNsolver introduces a deep learning approach for modeling polarization-sensitive diffractive neural networks, addressing the challenge of accurate and efficient electromagnetic coupling simulation within cascaded diffractive layers.