A novel guided deep learning algorithm to design low-cost SPP films
This work addresses cost reduction in SPP film design for materials science applications, representing an incremental improvement over standard methods.
The paper tackles the ill-posed inverse problem of designing low-cost surface plasmon polaritons (SPP) films by introducing a guided deep learning algorithm that reduces cost by replacing precious metals with ordinary metals, achieving an average relative error of less than 10% in spectrum prediction.
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.