Bayesian optimization design method of metal-dielectric multilayer columnar optical nano antenna

Abstract

The invention provides a bayesian optimization design method of a metal-dielectric multilayer columnar optical nano antenna, which comprises the following steps: S1, constructing a structure parametercandidate set according to antenna design requirements; s2, determining an optimization objective function as a far-field directivity coefficient, and setting a constraint function at the same time;s3, setting an initial training parameter set and obtaining a corresponding optimization objective function value and a corresponding constraint function value, taking the three sets as a training set, and setting an initial value of iteration times k; s4, training hyper-parameters in the Gaussian model according to the training set; s5, selecting a next candidate parameter; s6, calculating an objective function value and a constraint function value corresponding to the candidate parameter; and S7, judging whether the objective function values corresponding to the candidate parameters meetingthe constraint conditions meet cut-off conditions or not. The method has the advantages that the method is optimized through Bayesian optimization, the calculation time can be effectively shortened, the design efficiency is improved, and the purpose of quickly optimizing the antenna scattering efficiency is achieved.

Description

technical field [0001] The invention relates to antennas, in particular to a Bayesian optimal design method for metal-dielectric multilayer columnar optical nanometer antennas. Background technique [0002] With the gradual development of modern science and technology, wireless communication systems are developing rapidly towards high-speed, large-capacity transmission and other directions. The carrier wireless in the wireless communication system needs to meet the characteristics of low cost, high efficiency, portability, and miniaturization. [0003] The conventional antenna design method is to design the antenna structure, then adjust the structure parameters, and test the output efficiency. Since the relationship between the output efficiency of the antenna and the structural parameters is unknown and rarely satisfies a linear relationship, the adjustment of the structural parameters is random and tedious in this design process. Especially when encountering high-dimens...

AI Technical Summary

Problems solved by technology

Although these algorithms have the advantages of simple operation and fewer parameters, when dealing with non-convex, multi-peaked and difficult-to-derivate objective functions, these algorithms converge slowly and easily fall into the local optimal solution, resulting in the entire antenna design cycle. It takes a long time and cannot guarantee the high-efficiency output of the antenna

For antenna design with high-dimensional structural parameters, the calculation cycle is longer

Images