Diverse electromagnetic (EM) responses of coding metamaterials have been investigated, and the general research method is to use full-wave simulation. But if we only care its scattering properties, it is not necessary to perform full-wave simulation, which is usually time-consuming. Machine learning has significantly impelled the development of automatic design and optimize coding matrix. Based on metamaterial particle that has multiple response and genetic algorithm which is coupled with the scattering pattern analysis, we can optimize the coding matrix quickly to tailor the scattering properties without conducting full-wave simulation a lot of times for optimization. Since the coding matrix control of each particle allow modulation of EM wave, various EM phenomena can be achieved easier. In this paper, we proposed two reflective unitcells with different reflection phase, and then a semi-analytical model is built up for unitcells. To tailor the scattering properties, genetic algorithm normally based on binary coding, is coupled with the scattering pattern analysis in order to optimize the coding matrix. Finally, simulation results are compared with the semi-analytical calculation results and it is found that the simulation results agree very well with the theoretical values.
Towards robust cellular image classification: theoretical foundations for wide-angle
scattering pattern analysis
