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El aprendizaje profundo ha permitido el diseño inverso de medios dispositivos de silicio ultracompactos para la

Weifeng Jiang, Lin Zhang, Zhengguo Han

|February 20, 2026

Resumen

El aprendizaje profundo acelera el diseño de dispositivos fotónicos de silicio para la conversión de modo. Esta nueva plataforma permite el desarrollo rápido de dispositivos de alto rendimiento para óptica integrada.

Área de la Ciencia:

Fotónica y Ingeniería Óptica.
Ciencia de los materiales Ciencia de los materiales.
La inteligencia artificial es inteligencia artificial.

Sus antecedentes:

Los dispositivos fotónicos son cruciales para la comunicación óptica y la computación.
Los métodos de diseño tradicionales para dispositivos fotónicos a menudo consumen mucho tiempo y son complejos.
El aprendizaje profundo ofrece una vía prometedora para acelerar el proceso de diseño.

Objetivo del estudio:

Desarrollar una plataforma basada en una red neuronal profunda (DNN) para la predicción avanzada y el diseño inverso de dispositivos de silicio.
Para permitir un diseño rápido y eficiente de convertidores de modo de alto rendimiento.
Para validar las predicciones y diseños de la plataforma a través de la verificación experimental.

Principales métodos:

Desarrollo de una plataforma de red neuronal profunda (DNN) para predecir el rendimiento de dispositivos fotónicos.
Utilizando la plataforma DNN para el diseño inverso de convertidores de modo basados en silicio.
Fabricación experimental y caracterización de los convertidores de modos diseñados.

Principales resultados:

La plataforma DNN logró una rápida predicción de rendimiento (5 ms) con una alta precisión (RMSE < 0,018).
El diseño inverso produjo convertidores de modo de silicio ultracompactos (4,08 μm × 1,68 μm) en un tiempo mínimo (10 ms).
Los resultados experimentales confirmaron el rendimiento de los dispositivos diseñados, con pérdidas de inserción que coincidían estrechamente con las predicciones.

Conclusiones:

El enfoque basado en DNN acelera significativamente el desarrollo de convertidores de modo de silicio de alto rendimiento.
Esta plataforma proporciona componentes esenciales para sistemas avanzados de multiplexación por división de modos.
El estudio demuestra el potencial del aprendizaje profundo para el diseño inverso rápido en fotónica integrada.

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