## 4 Evaluation
### Optimal Design of Sensor Structure  
Based on the sensor optimization objective equation established in this paper, the sensor structure was analyzed and designed by using Particle Swarm Optimization (PSO). We set the input sensor interdigital logarithm to \(N=33\); the variation range of each geometric structure parameter is the same as the accuracy analysis, the ambient temperature to \(T=278.15\,\mathrm{K}\), and the ambient humidity as \(RH=10\%\). The optimization problem of the sensor electrode structure can be formulated as:  
\[\begin{array}{c}\min f(a,b,h_{1},h_{2},L,N,RH,T)\\ 3\leq a\leq 7\\ 6\leq b\leq 14\\ \text{subject to}\quad\begin{array}{c}1.8\leq h_{1}\leq 4.2\\ 750\leq L\leq 1750\\ 0.6\leq h_{2}\leq 1.4\end{array}\end{array} \tag{24}\]  
Figure 8: Dynamic response model accuracy evaluation.  
According to the monotonicity analysis between the sensitivity and adjustment time of the sensor and various structural parameters, it can be determined that when \(a\) takes the minimum value, and \(b\) takes the maximum value, the utmost sensitivity and the best dynamic performance of the sensor can be obtained. For \(h_{1}\) and \(h_{2}\), it is contradictory to improve the sensitivity and dynamic performance, and the sensor structure optimization scheme needs to be designed for different situations.  
When the structure of the electrode layer of the humidity sensor has been processed, the moisture-sensitive layer has not been uniformly coated and suspended. That is, \(h_{2}\) has been determined, and \(h_{1}\) is still unknown. Based on the established sensor optimization objective function, the PSO optimization algorithm is used to realize the optimal design of the thickness \(h_{1}\) of the moisture-sensitive layer. Figure 9 shows the iterative optimization process of the algorithm. After 2360 iterations, the optimal objective function \(f\) reaches the lowest value of \(-0.13\), and the optimal parameter result of \(h_{1}\) is 2.88 \(\upmu\)m. The sensor's sensitivity is \(1.20\times 10^{-13}\) F/%RH, and the dynamic response time is 2.84 s, which ensures the optimal sensitivity and dynamic response performance under this structure.  
When the humidity sensor polyimide thickness has been determined, the electrode structure has not been processed. That is, \(h_{1}\) has been determined, and \(h_{2}\) is still unknown. Figure 10 shows the iterative optimization process of plate thickness \(h_{2}\). After 1880 iterations, the optimal objective function \(f\) reaches the lowest value of \(-0.24\), and  
Figure 9: Optimum design of sensor moisture-sensitive layer.  
the optimal parameter result of \(h_{2}\) is 0.93 \(\upmu\)m. At this time, the sensor's sensitivity is \(1.22\times 10^{-13}\) F/%RH, and the dynamic response time is 3.01 s.  
Through the above discussion and analysis, we have completed the structural optimization design that considers the humidity sensor's sensitivity and dynamic response performance for different situations. To verify the accuracy and reliability of the scheme, we simulated and calculated the optimal objective function values under other sensor structures, as shown in Figure 11. It can be seen from the figure that the theoretical calculation result of PSO is the same as that of the simulation analysis and has high accuracy. Under this structural design, the objective function of the sensor has reached the minimum value, and the performance is optimal. Similarly, according to different scenarios and size requirements, the model established in this paper can flexibly change parameters to obtain an optimal sensor design scheme that meets actual needs.  
Figure 10: Optimum design of sensor electrode layer.