High-performance optical phased array for LiDARs demonstrated by monolithic integration of polymer and SiN waveguides

Lee, Eun-Su; Jin, Jinung; Chun, Kwon-Wook; Lee, Sang‐Shin; Oh, Min‐Cheol

doi:10.1364/oe.499868

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…It is an efficient compressive sensing method directly adoptable by LiDAR and other active imaging systems for medical, surveillance, astronomy, and environmental applications. In particular, we see PI-MAE flourishing in the autonomous driving industry, as the adoption of integrated photonic circuits of Optical Phased Arrays (OPAs) for Li-DAR applications has spread widely over recent years 53,54 . The non-resonant, non-mechanical features of OPAs complement PI-MAE's random physically down-sampled imaging methodology.…”

Section: Discussionmentioning

confidence: 99%

Physics-Informed Masked Autoencoder for Active Sparse Imaging

McEvoy,

Tafone,

Sua

et al. 2024

Preprint

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Imaging technology based on detecting individual photons has seen tremendous progress in recent years, with broad applications in autonomous driving, biomedical imaging, astronomical observation, and more. Comparing with conventional methods, however, it takes much longer time and relies on sparse and noisy photon-counting data to form an image. Here we introduce Physics-Informed Masked Autoencoder (PI-MAE) as a fast and efficient approach for data acquisition and image reconstruction through hardware implementation of the MAE (Masked Autoencoder). We examine its performance on a single-photon LiDAR system when trained on digitally masked MNIST data. Our results show that, with 1.8 × 10−6 or less detected photons per pulse and down to 9 detected photons per pixel, it achieves high-quality image reconstruction on unseen object classes with 90% physical masking. Our results highlight PI-MAE as a viable hardware accelerator for significantly improving the performance of single-photon imaging systems in photon-starving applications.

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Section: Discussionmentioning

confidence: 99%

Physics-Informed Masked Autoencoder for Active Sparse Imaging

McEvoy,

Tafone,

Sua

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Physics-Informed Masked Autoencoder for active sparse imaging

McEvoy,

Tafone,

Sua

et al. 2024

Sci Rep

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Imaging technology based on detecting individual photons has seen tremendous progress in recent years, with broad applications in autonomous driving, biomedical imaging, astronomical observation, and more. Comparing with conventional methods, however, it takes much longer time and relies on sparse and noisy photon-counting data to form an image. Here we introduce Physics-Informed Masked Autoencoder (PI-MAE) as a fast and efficient approach for data acquisition and image reconstruction through hardware implementation of the MAE (Masked Autoencoder). We examine its performance on a single-photon LiDAR system when trained on digitally masked MNIST data. Our results show that, with $$1.8\times 10^{-6}$$ 1.8 × 10 - 6 or less detected photons per pulse and down to 9 detected photons per pixel, it achieves high-quality image reconstruction on unseen object classes with 90% physical masking. Our results highlight PI-MAE as a viable hardware accelerator for significantly improving the performance of single-photon imaging systems in photon-starving applications.

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Parallel indirect time-of-flight ranging using on-chip dual-frequency combs

Gerguis,

Othman,

2024

Appl. Opt.

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The significant advancements in autonomous vehicle applications demand detection solutions capable of swiftly recognizing and classifying objects amidst rapidly changing and low-visibility conditions. Light detection and ranging (LiDAR) has emerged as a robust solution, overcoming challenges associated with camera imaging, particularly in adverse weather conditions or low illumination. Rapid object recognition is crucial in dynamic environments, but the speed of conventional LiDARs is often constrained by the 2D scanning of the laser beam across the entire scene. In this study, we introduce a parallelization approach for the indirect time-of-flight (iToF) ranging technique. This method enables efficient and high-speed formation of 1D clouds, offering the potential to have extended range capabilities without being constrained by the laser coherence length. The application potential spans mid-range autonomous vehicles ranging to high-resolution imaging. It utilizes dual-frequency combs with slightly different repetition rates. The method leverages the topology of the target object to influence the phase of the beating signal between the comb lines in the RF domain. This approach enables parallel ranging in one direction, confining the scanning process to a single dimension, and offers the potential for high-speed LiDAR systems. A tri-comb approach will be discussed that can provide an extended unambiguous range without compromising the resolution due to the range–resolution trade-off in iToF techniques. The study starts by explaining the technique for parallel detection of distance and velocity. It then presents a theoretical estimation of phase noise for dual combs, followed by an analysis of distance and velocity detection limits, illustrating their maximum and minimum extents. Finally, a study on the mutual interference conditions between two similar LiDAR systems is presented, demonstrating the feasibility of designing simultaneously operating LiDARs to avoid mutual interference.

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High-performance optical phased array for LiDARs demonstrated by monolithic integration of polymer and SiN waveguides

Cited by 5 publications

References 41 publications

Physics-Informed Masked Autoencoder for Active Sparse Imaging

Physics-Informed Masked Autoencoder for Active Sparse Imaging

Physics-Informed Masked Autoencoder for active sparse imaging

Parallel indirect time-of-flight ranging using on-chip dual-frequency combs

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