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A photonic complex perceptron for ultrafast data processing.

Mattia Mancinelli1, Davide Bazzanella2, Paolo Bettotti2

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We developed a complex-valued photonic perceptron using silicon photonics for optical data processing. This passive circuit avoids signal attenuation by training only phase, achieving high-speed binary pattern recognition.

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Area of Science:

  • Photonics
  • Optical Computing
  • Artificial Intelligence

Background:

  • The perceptron is a fundamental component in photonic neural networks.
  • Processing data in the optical domain offers potential for high speed and low power consumption.

Purpose of the Study:

  • To describe and demonstrate a complex-valued photonic perceptron.
  • To integrate time and space multiplexing in a passive silicon photonics circuit.
  • To process data in the optical domain for neural network applications.

Main Methods:

  • A complex-valued photonic perceptron was designed and fabricated on a silicon photonics integrated circuit.
  • Time and space multiplexing were employed for data input.
  • A particle swarm algorithm was used to train the optical mode phases.
  • The perceptron was tested for binary pattern recognition and delayed XOR operations.

Main Results:

  • The photonic perceptron successfully processed data in the optical domain.
  • Signal attenuation was avoided by training only the phases of optical modes.
  • High-speed operations up to 16 Gbps were achieved.
  • Low Bit Error Rates were demonstrated for the performed tasks.

Conclusions:

  • A fully passive silicon photonics integrated circuit for a complex-valued perceptron was demonstrated.
  • The proposed photonic perceptron architecture is effective for optical data processing and neural network applications.
  • The method avoids signal attenuation, paving the way for efficient optical computing.