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Role of spatial coherence in diffractive optical neural networks.
Optics Express
|November 14, 2024
Summary
This study explores spatial coherence in diffractive optical neural networks (DONNs), crucial for real-world applications. We developed a simulation method to analyze DONN performance with varying light coherence for machine learning tasks.
Area of Science:
- Optics
- Machine Learning
- Computer Vision
Background:
- Diffractive optical neural networks (DONNs) offer fast, energy-efficient processing for machine learning.
- Existing DONN research primarily uses coherent light, limiting real-world applicability.
- Spatial coherence of optical signals is a critical factor for many DONN applications.
Purpose of the Study:
- To investigate the impact of spatial coherence on DONN performance.
- To develop an efficient numerical simulation for DONNs under various coherence conditions.
- To evaluate DONN capabilities with partially coherent and incoherent light.
Main Methods:
- Developed a numerical simulation approach for DONNs.
- Analyzed computational complexity for simulating different coherence levels.
- Trained and evaluated simulated DONNs on the MNIST dataset with varying spatial coherence.
Main Results:
- Demonstrated the ability to simulate DONNs with incoherent and partially coherent light.
- Quantified the effect of spatial coherence on DONN performance in simulations.
- Successfully processed handwritten digits from the MNIST dataset under varying coherence.
Conclusions:
- Spatial coherence significantly influences DONN performance.
- The developed simulation method enables efficient analysis of DONNs under realistic optical conditions.
- This work paves the way for DONNs in diverse real-world machine learning applications.
