Architecture class within ONNs.
Physics / mechanism
Origin: Lin et al. (Ozcan group, UCLA) demonstrated the first all-optical D2NN classifier in Science 2018, with cascaded 3D-printed phase plates performing MNIST-class classification at the speed of light through the stack. The architecture has since spread to USTC, Stanford, CMU, and many others.
Key parameters:
- Latency sub-ns per layer (light propagation through optical path)
- Energy dominated by source laser and detector array (not the diffraction itself, which is “free”)
- Precision historically the weak point — analog at every layer, error compounds with depth
- Reconfigurability is the major variable — fixed phase plates are fastest but task-locked; SLM-driven systems are slower (limited by SLM refresh) but reprogrammable per workload
Distinguished from sibling architectures
Why this matters now (2026)
Companies using
Connected ideas
Sources
Frontier (open questions)
See frontmatter frontier: block.
Reading list
- To be added — Ozcan UCLA originating Science 2018 paper + key follow-ups. Run
/kb-research diffractive-deep-neural-networksto populatesources/papers/.