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Photonic integrated systems can be harnessed for fast and efficient optical telecommunication and metrology technologies. Here the authors develop a dual-soliton microcomb technique for massively parallel coherent laser ranging that requires only a single laser and a single photoreceiver. Laser-based ranging (LiDAR) - already ubiquitously used in industrial monitoring, atmospheric dynamics, or geodesy - is a key sensor technology. Coherent laser ranging, in contrast to time-of-flight approaches, is immune to ambient light, operates continuous-wave allowing higher average powers, and yields simultaneous velocity and distance information. State-of-the-art coherent single laser-detector architectures reach hundreds of kilopixel per second sampling rates, while emerging applications - autonomous driving, robotics, and augmented reality - mandate megapixel per second point sampling to support real-time video-rate imaging. Yet, such rates of coherent LiDAR have not been demonstrated. Recent advances in photonic chip-based microcombs provide a route to higher acquisition speeds via parallelization but require separation of individual channels at the detector side, increasing photonic integration complexity. Here we overcome the challenge and report a hardware-efficient swept dual-soliton microcomb technique that achieves coherent ranging and velocimetry at megapixel per second line scan measurement rates with up to 64 optical channels. Multiheterodyning two synchronously frequency-modulated microcombs yields distance and velocity information of all individual ranging channels on a single receiver alleviating the need for individual separation, detection, and digitization. The reported LiDAR implementation is compatible with photonic integration and demonstrates the significant advantages of acquisition speed afforded by the convergence of optical telecommunication and metrology technologies.
Kirsten Emilie Moselund, Chang Won Lee
Tobias Kippenberg, Rui Ning Wang, Andrey Voloshin, Xinru Ji, Zheru Qiu, Andrea Bancora, Yang Liu