Prediction of photonic crystal emitter efficiency using an optimized fuzzy learning approach

Photonic crystals (PC) have attracted much attention over the last decade for their unique ability to control light propagation. Researchers suggested the use of metallic photonic crystal with network topology as high efficiency thermal emitters. A necessary precursor to the deployment of such crystals in practical systems is fast accurate prediction of the emission characteristics and efficiency from a photonic lattice. Conventional models that simulate the photonic response of PC are computationally expensive and can take up to a few hours on several parallel processors to realize the emitter efficiency for a given PC structure. Therefore, a practical design process with trial and error cannot be done in a reasonable amount of time.In this article we suggest the use of a fuzzy learning approach to establish a model that can be used to predict emitter efficiency from such systems. The widely studied metallic PC Lincoln log structure is used as a case study. We show that the proposed method can estimate the efficiency of any PC Lincoln log structure much faster than any existing method and is by no means bound to this specific geometry. The case study presented here was chosen only because of recent high interest in it and the abundance of literature data on the example structure. The learning process using Fuzzy set Theory is explained. A multi-objective optimization method to enhance the fuzzy learning process is also outlined. An exemplar case showing the ability of the proposed model to predict the emitter efficiency of a tungsten PC with a bandgap at (10–11.5 μm) is illustrated. We show that once the fuzzy learning is performed, the proposed method can predict the emitter efficiency with 95% accuracy without the need for any expensive computations.