A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures

By Wei Ma, Yongmin Liu. 2020

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yihao Xu
2. Xianzhe Zhang
3. Peter R. Wiecha
4. Yun Raymond Fu
5. A. Arbouet
6. O. Muskens
7. C. Girard
8. Alexandre Vial

Citations in the corpus

• Deep learning in nano-photonics: inverse design and beyond (2020, background): “Afurther type of one-to-many solving networks is conditional adversarial or conditional variational autoencoders [66,77 – 80].”
• Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks (2021, background): “Very recently, W. Ma and Y. Liu developed a semi-supervised learning strategy to accelerate the training data generation process, the most time-consuming part of the deep-learning-aided inverse design [78].”