Multifunctional inverse sensing by spatial distribution characterization of scattering photons

Abstract

Inverse sensing is an important research direction to provide new perspectives for optical sensing. For inverse sensing, the primary challenge is that scattered photon has a complicated profile, which is hard to derive a general solution. Instead of a general solution, it is more feasible and practical to derive a solution based on a specific environment. With deep learning, we develop a multifunctional inverse sensing approach for a specific environment. This inverse sensing approach can reconstruct the information of scattered photons and characterize multiple optical parameters simultaneously. Its functionality can be upgraded dynamically after learning more data. It has wide measurement range and can characterize the optical signals behind obstructions. The high anti-noise performance, flexible implementation, and extremely high threshold to optical damage or saturation make it useful for a wide range of applications, including self-driving car, space technology, data security, biological characterization, and integrated photonics.

Keywords

• deep learning
• optical sensing
• photonics