All-optical Fourier neural network using partially coherent light

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 used in optical neural networks, our study proposed harnessing spatially incoherent light in all-optical Fourier neural networks. Contrary to natural predictions of declining target recognition accuracy with increased incoherence, our experimental results demonstrated a surprising outcome: improved accuracy with incoherent light. We attribute this enhancement to spatially incoherent light's ability to alleviate experimental errors like diffraction rings and laser speckle. Our experiments introduced controllable 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, including on-chip optical computing, photonic interconnects, and reconfigurable optical processors.