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    Area of Science:

    • Photonics and optical sensing
    • Artificial intelligence in sensing
    • Smartphone sensor technology

    Background:

    • Direct time-of-flight (D-TOF) Light Detection and Ranging (LIDAR) systems are crucial for accurate depth sensing.
    • Single-photon avalanche diode (SPAD) based LIDAR performance is fundamentally linked to photon-counting statistics and system parameters.
    • Achievable LIDAR performance depends on the chosen depth estimation method, not solely on intrinsic system capabilities.

    Purpose of the Study:

    • To evaluate D-TOF LIDAR systems for smartphone applications, focusing on parameter trade-offs and estimation efficiency.
    • To analyze the impact of system parameters, application constraints, and detection nonlinearities on LIDAR performance.
    • To assess an artificial intelligence (AI)-based depth estimation method against theoretical performance limits.

    Main Methods:

    • Development of a simulation model integrating radiometry and photon-counting statistics.
    • Conducting a trade-off analysis to understand parameter dependencies and nonlinearities.
    • Derivation of an analytical model for the Cramér-Rao lower bound (CRLB), incorporating shot noise.
    • Evaluation of an AI-based depth estimation technique and comparison with the CRLB.

    Main Results:

    • The simulation and analytical models provide insights into D-TOF LIDAR performance trade-offs for smartphone applications.
    • The Cramér-Rao lower bound (CRLB) was analytically derived, accounting for shot noise limitations.
    • The AI-based depth estimation method demonstrated superior performance, compensating for nonlinearities.
    • The AI estimator's effectiveness was shown to be robust across different application conditions, including varying target reflectivity.

    Conclusions:

    • AI-based depth estimation significantly enhances D-TOF LIDAR performance in smartphones by mitigating system nonlinearities.
    • The developed models and analysis provide a framework for optimizing LIDAR system design and depth estimation strategies.
    • This research highlights the potential of AI to push the boundaries of achievable depth sensing accuracy in mobile applications.