Photodetection
Cross-source consensus on Photodetection from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Risks & contraindications
Highlighted claims
- Photodetection supplies the main nonlinearity by measuring intensity as a quadratic operation in field amplitude. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- Class scores are computed by integrating optical energy over disjoint detector regions assigned to classes. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- The predicted class is selected by taking the argmax over region-integrated energies. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- Correct classifications arise when learned phase structures create constructive interference in the correct detector region and destructive interference elsewhere. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification
- Detection noise is not modeled, although shot noise and read noise may matter in low-power or miniaturized systems. — Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification