Work

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

Year: 2026

Type: article

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.

Source: npj Nanophotonics

DOI: 10.1038/s44310-026-00146-0

Authors: Yujin Park, Yewon Han, Seokwoo Kim, Joohoon Kim, Junsuk Rho

Institutions: Pohang University of Science and Technology, Pohang Iron and Steel (South Korea), Research Institute of Industrial Science and Technology

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,090

Funders: National Research Foundation, Pohang University of Science and Technology, National Research Foundation of Korea, Ministry of Science and ICT, South Korea