Integrated photonic metasystem for image classifications at telecommunication wavelength

Abstract Miniaturized image classifiers are potential for revolutionizing their applications in optical communication, autonomous vehicles, and healthcare. With deep diffractive neuron networks trained subwavelength structures, we demonstrate image recognitions by a passive silicon photonic metasystem. The metasystem implements high-throughput vector-by-matrix multiplications, enabled by 103 passive subwavelength phase shifters as weight elements in 1 mm2 footprint. The large weight matrix size incorporates the fabrication variation related uncertainties, and thus the pre-trained metasystem can perform machine learning tasks without post-tuning. A 15-pixel spatial pattern classifier reaches near 90% accuracy with femtosecond inputs. The metasystem’s superior parallelism (1015 bit/s) dramatically expand data processing capability of photonic integrated circuits, towards next generation low latency and low power photonic accelerators compatible with complementary metal-oxide-semiconductor manufacturing.