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August 20, 2026npj Nanophotonics0 citationsOpen Access

Free-space and on-chip diffractive deep neural network for next-generation optical computing

YPYujin ParkYHYewon HanSKSeokwoo Kim

Key Points

  • Summarize the core principles, design methodologies, and recent advances in free-space and on-chip diffractive deep neural networks for next-generation optical processing.
  • Reviewed foundational physics and design frameworks for multi-layered trainable diffractive neurons operating via light diffraction and interference.
  • Surveyed deployment strategies across computational tasks including image classification, 3D perception, and edge computing.
  • Diffractive neural architectures perform deep network operations passively at the speed of light by exploiting intrinsic optical parallelism.
  • Optical setups achieve ultrafast inference speeds and virtually zero computational energy consumption during light propagation.

Abstract

Diffractive deep neural networks (D 2 NNs), composed of multiple layers of trainable diffractive neurons, perform deep network operations entirely through the diffraction and interference of light. This approach has been widely studied in classification, 3D perception, and edge learning due to its zero-power consumption, ultrafast processing, and intrinsic parallelism of light. This review presents fundamental principles, design methodologies, and recent advances in D 2 NNs for next generation optical computing.

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Cite This Study

Park et al. (2026) studied this question.

synapsesocial.com/papers/6a86b6208a91293e6a1cdc1dhttps://doi.org/10.1038/s44310-026-00146-0
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