A Comb-based Colorless Coherent WDM Transmitter

We propose a comb-based WDM transmitter capable of modulating independent signals to comb lines without demultiplexing them and prove its concept and potential scalability in a WDM transmitter consisting of a Kerr microcomb and a silicon I/Q modulator array.

A 262 TOPS Hyperdimensional Photonic AI Accelerator powered by a Si3N4 microcomb laser

The ever-increasing volume of data has necessitated a new computing paradigm, embodied through Artificial Intelligence (AI) and Large Language Models (LLMs). Digital electronic AI computing systems, however, are gradually reaching their physical plateaus, stimulating extensive research towards next-generation AI accelerators. Photonic Neural Networks (PNNs), with their unique ability to capitalize on the interplay of multiple physical dimensions including time, wavelength, and space, have been brought forward with a credible promise for boosting computational power and energy efficiency in AI processors. In this article, we experimentally demonstrate a novel multidimensional arrayed waveguide grating router (AWGR)-based photonic AI accelerator that can execute tensor multiplications at a record-high total computational power of 262 TOPS, offering a ~24x improvement over the existing waveguide-based optical accelerators. It consists of a 16x16 AWGR that exploits the time-, wavelength- and space- division multiplexing (T-WSDM) for weight and input encoding together with an integrated Si3N4-based frequency comb for multi-wavelength generation. The photonic AI accelerator has been experimentally validated in both Fully-Connected (FC) and Convolutional NN (NNs) models, with the FC and CNN being trained for DDoS attack identification and MNIST classification, respectively. The experimental inference at 32 Gbaud achieved a Cohen's kappa score of 0.867 for DDoS detection and an accuracy of 92.14% for MNIST classification, respectively, closely matching the software performance.

Hardware-efficient megapixel per second coherent soliton microcomb ranging

Laser based ranging (LiDAR) - already ubiquitously used in robotics, industrial monitoring, or geodesy - is a key sensor technology for future autonomous driving, and has been employed in nearly all successful implementations of autonomous vehicles to date. Coherent laser allows long-range detection, operates eye safe, is immune to crosstalk and yields simultaneous velocity and distance information. Yet for actual deployment in vehicles, video frame-rate requirements for object detection, classification and sensor fusion mandate megapixel per second measurement speed. Such pixel rates are not possible to attain with current coherent single laser-detector architectures at high definition range imagining, and make parallelization essential. A megapixel class coherent LiDAR has not been demonstrated, and is still impeded by the arduous requirements of large banks of detectors and digitizers on the receiver side, that need to be integrated on chip. Here we report hardware efficient coherent laser ranging at megapixel per second imaging rates. This is achieved using a novel concept for massively parallel coherent laser ranging that requires only a single laser and a single photoreceiver, yet achieves simultaneous recording of more than 64 channels with distance and velocity measurements each - attaining an unprecedented 5 megapixel per second rate. Heterodyning two offset chirped soliton microcombs on a single coherent receiver yields an interferogram containing both distance and velocity information of all particular channels, thereby alleviating the need to individually separate, detect and digitize distinct channels. The reported LiDAR implementation is hardware-efficient, compatible with photonic integration and demonstrates the significant advantages of acquisition speed, complexity and cost benefits afforded by the convergence of optical telecommunication and metrology technologies.