## 4 Structural Optimization  
The structure optimization is carried out to optimize the absorption of the solar absorber design. There are different methods to optimize the structure. One of the important methods is parametric optimization. Parametric optimization is used to optimize the results while keeping the different constraints in consideration. The behavior of the system is nonlinear, so nonlinear optimization algorithms can be used to optimize the different parameters. In this section, we have detailed the optimization method and also presented the optimized results using numerical investigation. The optimization of the structure is generally done with different types of optimization. One of the important optimizations is parametric optimization. We have explained the parametric optimization here in detail before we present the structure optimization results. Parametric optimization is one of the important methods to optimize the results of the solar absorber design [29]. Nonlinear optimization is utilized in situations in which the function does not behave linearly in relation to the variable being optimized for. Nonlinear optimization is having function f(x), or constraint c\({}_{i}\)(x) = 1, 2,...n or d\({}_{j}\)(x) = 0, 1,...n, are nonlinear functions of the vector of variables x. Several researchers make use of the nonlinear optimization method in order to enhance the results obtained from the nonlinear system [30]. The gradient nonlinear optimization method is also one of the useful methods for determining the maximal nonlinear systems for energy conversion [31]. In our research, we have used a nonlinear optimization algorithm as our research shows nonlinear behavior. The absorption is the result that needs to be maximized, while the parameter that needs to be optimized has different physical and geometrical parameters. The research is also using wavelength to observe the maximum absorption while varying the different geometrical parameters. The results related to the optimization are presented in Figures 4 and 5 and discussed in detail in this section. The degree of influence of the optimization algorithm is also discussed in this section. Figure 4: The impact of SiO\({}_{2}\) thickness change on absorption is shown in (**a**) the absorption response for 500 nm to 2500 nm SiO\({}_{2}\) thickness variation in a line plot and (**b**) the absorption response for 500 nm to 2500 nm variation in a color plot, with red having the highest absorption. The high absorption regions are marked in circles at 2500 nm. The high absorption is also visible in the line plot, with the green color line having a 2500 nm value. The structural optimization is carried out on the two dielectric materials used as substrates in this design. First, the SiO\({}_{2}\) substrate height is varied from 500 nm to 2500 nm. The response to this change is investigated in Figure 4. The result shows that the highest thickness of 2500 nm has more absorption compared to the other thicknesses. The absorption is higher in this thickness because of the higher area provided for the absorption of the signal. The red part of Figure 4b, marked with circles, shows the high absorption regions in the 2500 nm thickness range. The line plot also shows that high absorption for this thickness is available at three different locations, as presented in the color plot. The increase  
Figure 5: The impact of GaAs thickness change on absorption is shown in (**a**) the absorption response for 250 nm to 1000 nm GaAs thickness variation in a line plot and (**b**) the absorption response for 250 nm to 1000 nm variation in a color plot, with the red color having the highest absorption. The absorption is nearly constant across the entire range of GaAs material thickness variations. in further thickness is not checked because the higher thickness is making the area and size of the structure large as well as increasing the inductance, which affects the results.