All-optical Fourier neural network using partially coherent light

TL;DR

This study demonstrates that using spatially incoherent light in all-optical Fourier neural networks can unexpectedly improve recognition accuracy and robustness by reducing experimental errors.

Contribution

It introduces the novel use of partially coherent light in optical neural networks, challenging the assumption that coherence is always beneficial.

Findings

Incoherent light enhances recognition accuracy.

Incoherent light reduces diffraction and speckle effects.

Experimental results outperform numerical predictions.

Abstract

Optical neural networks present distinct advantages over traditional electrical counterparts, such as accelerated data processing and reduced energy consumption. While coherent light is conventionally employed in optical neural networks, our study proposes harnessing spatially incoherent light in all-optical Fourier neural networks. Contrary to numerical predictions of declining target recognition accuracy with increased incoherence, our experimental results demonstrate a surprising outcome: improved accuracy with incoherent light. We attribute this unexpected enhancement to spatially incoherent light's ability to alleviate experimental errors like diffraction rings, laser speckle, and edge effects. Our controlled experiments introduced spatial incoherence by passing monochromatic light through a spatial light modulator featuring a dynamically changing random phase array. These findings underscore partially coherent light's potential to optimize optical neural networks, delivering dependable and efficient solutions for applications demanding consistent accuracy and robustness across diverse conditions.