Self-learning photonic signal processor with an optical neural network chip

Abstract

Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, we report and experimentally demonstrate a fully self-learning and reconfigurable photonic signal processor based on an optical neural network chip. The proposed photonic signal processor is capable of performing various functions including multichannel optical switching, optical multiple-input-multiple-output descrambler and tunable optical filter. All the functions are achieved by complete self-learning. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with artificial intelligence.

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Cited by in corpus (6)

• Single chip photonic deep neural network with accelerated training
• 20-Mode Universal Quantum Photonic Processor
• Chip-to-chip optical multimode communication with universal mode processors
• Fast-response silicon photonic microheater induced by parity-time symmetry breaking
• Self-configuring high-speed multi-plane light conversion
• Independently reconfigurable internal loss and resonance-shift in an interferometer-embedded optical cavity