LSTM-based autoencoder for the inverse design of achromatic metalenses

Prajith Pillai; Beena Rai; Aravind Yelashetty; Tapajyoti Das Gupta; Parama Pal

doi:10.1117/12.2649737

15 March 2023 LSTM-based autoencoder for the inverse design of achromatic metalenses

Prajith Pillai, Beena Rai, Aravind Yelashetty, Tapajyoti Das Gupta, Parama Pal

Proceedings Volume 12438, AI and Optical Data Sciences IV; 1243812 (2023) https://doi.org/10.1117/12.2649737
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

We describe an LSTM-based autoencoder for inversely designing an achromatic metalens comprised of cylindrical unit cells. The training data for our model has phase and transmission values corresponding to the heights and radii of each meta-unit. We use multiple data sequences (phase and transmission) to train the model and a multi-output model framework. The autoencoder is trained for 2500 iterations using the Adam optimizer with a learning rate of 0.001 and is subsequently used for inversely predicting the meta-unit dimensions at each radial position of the lens. Our model is validated via simulations as well as experiments.

Citation Download Citation

Prajith Pillai, Beena Rai, Aravind Yelashetty, Tapajyoti Das Gupta, and Parama Pal "LSTM-based autoencoder for the inverse design of achromatic metalenses", Proc. SPIE 12438, AI and Optical Data Sciences IV, 1243812 (15 March 2023); https://doi.org/10.1117/12.2649737

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