Deep-learning-based model for the inverse design of a reconfigurable multi-tap optical filter

Area of Science:

• Photonics and Optical Engineering
• Artificial Intelligence in Engineering
• Integrated Optics

Background:

• Integrated and reconfigurable optical filters are crucial components in modern communication and signal processing.
• The inverse design of these optical filters, which determines their structure from desired spectral characteristics, presents a significant challenge due to non-uniqueness issues.

Purpose of the Study:

• To develop an advanced deep learning model for the inverse design of multi-tap reconfigurable optical filters.
• To address the inherent non-uniqueness problem in optical filter inverse design.
• To enable precise prediction of control parameters for achieving desired filter spectra.

Main Methods:

• Implementation of an anti-non-uniqueness deep learning network based on an improved tandem neural network architecture.
• Utilizing eight optical switches to configure a tunable optical delay line, enabling up to 256 filter taps.
• Optimization of the neural network's loss function to enhance prediction accuracy.

Main Results:

• The deep learning model accurately determines the relationship between control parameters and spectral features of the optical filter.
• Precise predictions were achieved despite the multi-solution nature of the inverse design problem.
• The model demonstrated effectiveness in facilitating the practical application of reconfigurable optical filters.

Conclusions:

• The proposed deep learning approach effectively solves the inverse design problem for multi-tap reconfigurable optical filters.
• This method overcomes the challenge of non-uniqueness, paving the way for more efficient optical system design.
• The findings support the practical implementation of advanced reconfigurable optical filters in communication systems.