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Inverse-Designed On-Chip Orbital Angular Momentum Mode Converter for Optical Convolution Acceleration.

Yumeng Chen1, Kuo Zhang2, Kun Liao1

  • 1State Key Laboratory for Mesoscopic Physics & Department of Physics, Collaborative Innovation Center of Quantum Matter & Frontiers Science Center for Nano-optoelectronics Peking University Beijing China.

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Summary
This summary is machine-generated.

Researchers developed an on-chip device for orbital angular momentum (OAM) conversion and multiplexing, enabling efficient optical neural network operations. This breakthrough integrates OAM modes for advanced photonic computing applications.

Keywords:
inverse designoptical convolutionoptical neural networkorbital angular momentum

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Area of Science:

  • Photonics
  • Optical Computing
  • Artificial Intelligence

Background:

  • Optical neural networks utilize light's parallelism for enhanced computation.
  • Orbital Angular Momentum (OAM) offers vast potential for optical neural networks due to its infinite orthogonal modes.
  • On-chip OAM conversion and multiplexing are critical but challenging for integrated photonic devices.

Purpose of the Study:

  • To present an innovative on-chip device for OAM mode conversion and multiplexing.
  • To demonstrate the device's capability in constructing an OAM-encoded hybrid optical convolutional neural network.
  • To explore a practical pathway for integrating OAM with on-chip optical neural networks.

Main Methods:

  • Device fabrication using inverse design principles for OAM mode conversion and multiplexing.
  • Characterization of OAM conversion efficiencies and modulation depths for various OAM states.
  • Implementation of an OAM-encoded hybrid optical convolutional neural network using the fabricated device.

Main Results:

  • Achieved high OAM up-conversion efficiency (88.68%) and down-conversion efficiency (88.04%).
  • Demonstrated high multiplexer conversion efficiency (98.29%) and modulation depth (20.69 dB).
  • Attained 98.0% accuracy on MNIST and 86.1% accuracy on Fashion-MNIST using the OAM-encoded CNN.

Conclusions:

  • The developed device offers an effective solution for on-chip OAM conversion and multiplexing.
  • The device enables on-chip optical convolution operations, advancing integrated optical neural networks.
  • This work provides a significant step towards practical OAM integration in photonic computing.