A novel guided deep learning algorithm to design low-cost SPP films
arXiv:1912.03452
Abstract
The design of surface plasmon polaritons (SPP) films is an ill-posed inverse problem. There are many-to-one correspondence between the structures and user needs. We present a novel guided deep learning algorithm to find optimal solutions (with both high accuracy and low cost). To achieve this goal, we use low cost sample replacement algorithm in training process. The deep CNN would gradually learn better model from samples with lower cost. We have successfully applied this algorithm to the design of low-cost SPP films. Our model learned to replace precious metals with ordinary metals to reduce cost. So the the cost of predicted structure is much lower than standard deep CNN. And the average relative error of spectrum is less than 10%. The source codes are available at https://github.com/closest-git/MetaLab.
arXiv comment9 pages,6 figures
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Training deep neural networks for the inverse design of nanophotonic structures
- Multilayer optical calculations
- Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks
- Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
- Deep Learning for Design and Retrieval of Nano-photonic Structures
- Deep differentiable forest with sparse attention for the tabular data