Machine Learning in Interpolation and Extrapolation for Nanophotonic Inverse Design

Area of Science:

• Nanophotonics
• Computational electromagnetics
• Machine learning

Background:

• Algorithmic design of nanophotonic structures is crucial for component efficiency.
• Current methods are computationally intensive and may not yield globally optimal solutions.
• Machine learning offers a promising avenue to accelerate design and improve modeling.

Purpose of the Study:

• To explore artificial neural network (ANN) techniques for enhancing nanophotonic structure design.
• To evaluate the performance of different ANN approaches in interpolation and extrapolation.
• To investigate the potential of ANNs for generating novel nanophotonic structures.

Main Methods:

• Utilized artificial neural networks (ANNs) for the forward design of absorbing nanophotonic structures.
• Compared standard ANNs with combined classical machine learning techniques.
• Investigated the impact of pretraining on general image classification tasks.
• Evaluated deep neural networks against more complex architectures (convolutional, autoencoder layers).

Main Results:

• ANNs significantly reduce the time required for predicting nanophotonic structure responses.
• Pretrained networks demonstrate strong performance in both interpolative and extrapolative prediction with minimal training.
• Standard deep neural networks outperform complex architectures in extrapolation.
• ANNs successfully generate structures with spectral responses beyond the training data range.

Conclusions:

• ANNs provide a computationally efficient method for nanophotonic structure design.
• Pretrained ANNs offer a robust approach for predicting optical responses and enabling extrapolation.
• Deep neural networks are particularly effective for extrapolation tasks in nanophotonics.
• This work demonstrates the potential of ANNs for generating novel nanophotonic structures with tailored spectral responses.