Harnessing disordered photonics via multi-task learning towards intelligent four-dimensional light field sensors

Abstract

Abstract The complete description of a continuous-wave light field includes its four fundamental properties: wavelength, polarization, phase and amplitude. However, the simultaneous measurement of a multi-dimensional light field of such four degrees of freedom is challenging in conventional optical systems requiring a cascade of dispersive and polarization elements. In this work, we demonstrate a disordered-photonics-assisted intelligent four-dimensional light field sensor. This is achieved by discovering that the speckle patterns, generated from light scattering in a disordered medium, are intrinsically sensitive to a high-dimension light field given their high structural degrees of freedom. Further, the multi-task-learning deep neural network is leveraged to process the single-shot light-field-encoded speckle images free from any prior knowledge of the complex disordered structures and realizes the high-accuracy recognition of full-Stokes vector, multiple orbital angular momentum (OAM), wavelength and power. The proof-of-concept study shows that the states space of four-dimensional light field spanning as high as 1680=4 (multiple-OAM) $$\times$$ × 2 (OAM power spectra) $$\times$$ × 15 (multiple-wavelength) $$\times$$ × 14 (polarizations) can be well recognized with high accuracy in the chip-integrated sensor. Our work provides a novel paradigm for the design of optical sensors for high-dimension light fields, which can be widely applied in optical communication, holography, and imaging.

Keywords

• Disordered photonics
• Liquid crystals
• Light field detection
• Deep learning