Miniaturized image classifiers are potential for revolutionizing their applications in optical communication, autonomous vehicles, and healthcare. With subwavelength structure enabled directional diffraction and dispersion engineering, the light propagation through multi-layer metasurfaces achieves wavelength-selective image recognitions on a silicon photonic platform at telecommunication wavelength. The metasystems implement high-throughput vector-by-matrix multiplications, enabled by near 103 nanoscale phase shifters as weight elements within 0.135 mm2 footprints. The diffraction manifested computing capability incorporates the fabrication and measurement related phase fluctuations, and thus the pre-trained metasystem can handle uncertainties in inputs without post-tuning. Here we demonstrate three functional metasystems: a 15-pixel spatial pattern classifier that reaches near 90% accuracy with femtosecond inputs, a multi-channel wavelength demultiplexer, and a hyperspectral image classifier. The diffractive metasystem provides an alternative machine learning architecture for photonic integrated circuits, with densely integrated phase shifters, spatially multiplexed throughput, and data processing capabilities.

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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.

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We utilize machine learning algorithm to design integrated photonic meta-systems, and experimentally demonstrated the device performance.