Inverse Design of Distributed Bragg Reflectors Using Deep Learning

Distributed Bragg Reflectors are optical structures capable of manipulating light behaviour, which formed by stacking layers thin-film materials. The inverse design such is desirable, but not straightforward using conventional numerical methods. This study explores the application Deep Learning to a six-layer system, through implementation Tandem Neural Network. challenge split into three sections: generation training data Transfer Matrix method, Simulation Network (SNN) maps structural geometry spectral output, and finally an Inverse Design (IDNN) predicts required produce target spectra. latter enables designer develop custom multilayer systems with desired reflection properties. SNN achieved average accuracy 97% across dataset, IDNN achieving 94%. By this custom-made reflectors can be manufactured in milliseconds, significantly reducing cost generating photonic devices optics.