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Aprendizaje automático totalmente óptico utilizando redes neuronales profundas difractivas

Xing Lin1,2,3, Yair Rivenson1,2,3, Nezih T Yardimci1,3

  • 1Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA.

Science (New York, N.Y.)
|July 28, 2018
PubMed
Resumen
Este resumen es generado por máquina.

Los investigadores desarrollaron una red neuronal profunda difractiva física (D2NN) para el aprendizaje automático totalmente óptico. Este dispositivo óptico impreso en 3D realiza cálculos complejos a la velocidad de la luz, lo que permite nuevas aplicaciones ópticas.

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Área de la Ciencia:

  • Óptica y fotónica
  • Inteligencia artificial
  • Aprendizaje automático

Sus antecedentes:

  • Los modelos de aprendizaje profundo sobresalen en tareas de inferencia complejas.
  • El aprendizaje profundo tradicional se basa en el cómputo electrónico.
  • La computación óptica ofrece potencial para el procesamiento de alta velocidad.

Objetivo del estudio:

  • Introducir un mecanismo físico para el aprendizaje automático utilizando una red neuronal profunda difractiva totalmente óptica (D2NN).
  • Demostrar la capacidad de la D2NN para realizar varias funciones basadas en diseños de aprendizaje profundo.
  • Explorar aplicaciones en el análisis óptico de imágenes y el diseño de componentes.

Principales métodos:

  • Diseñó una arquitectura D2NN utilizando capas difractivas pasivas.
  • D2NN fabricados impresos en 3D para su validación experimental.
  • D2NN probados para la clasificación de imágenes y la funcionalidad de lentes de imagen en el espectro de terahertz.

Principales resultados:

  • Clasificación de imágenes implementada con éxito para dígitos escritos a mano y productos de moda.
  • Demostró el D2NN como una lente de imagen en el espectro de terahertz.
  • Logró la ejecución totalmente óptica de funciones complejas a la velocidad de la luz.

Conclusiones:

  • El marco D2NN totalmente óptico ofrece un nuevo paradigma para el aprendizaje automático de alta velocidad.
  • Las aplicaciones potenciales incluyen el análisis de imágenes totalmente ópticas, la detección de características y la clasificación de objetos.
  • Permite nuevos diseños de cámaras y componentes ópticos con funcionalidades únicas.