# Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
## 3 Model and Analysis
### Experimental Results and Analysis  
The experiments show that in the X-band, at the resonance point near the center frequency of 10 GHz, inputting the value 52 into the neural network model, the simulation and prediction results of return loss in figure (left) are both close to \(-25\) dB, and the average error between them is within 0.2 dB, inputting the value 674 into the neural network model, the simulation and prediction results of return loss in figure (right) are both close to \(-24\) dB, and the average error between them is within 0.3 dB. The prediction results of the return loss neural network are shown in Figure 9.  
Experiments show that in the X-band, at the resonance point near the center frequency of 10 GHz inputting the value 193 into the neural network model, the simulation and prediction results of insertion loss in figure (left) are both close to \(-0.2\) dB, and the average error between them is within 0.05 dB, inputting the value 682 into the neural network model, the simulation and prediction results of insertion loss in figure (right) are both close to \(-0.16\) dB, and the average error between them is within 0.1 dB and the insertion loss neural network prediction. The results are shown in Figure 10 below.  
Figure 8: Schematic diagram of loss function curve.  
Figure 9: Diagram of the prediction result of echo loss neural network, they should be listed as: (**left**) The result of the first random draw. (**right**) The result of the second random selection.  
The experiments show that in the X-band, at the resonance point near the center frequency of 10 GHz, inputting the value 86 into the neural network model, the simulation and prediction results of VSWR in figure (left) are both close to 1.08 dB, and the average error between them is within 0.02 dB, inputting the value 678 into the neural network model, the simulation and prediction results of return loss in figure (right) are both close to 1.22 dB, and the average error between them is within 0.1 dB. The prediction results of VSWR neural network are as follows Figure 11 shows.  
As can be seen from Figures 9-11 above, the neural network learns the microwave transmission characteristics of the S-parameters of a single wire bonded interconnect line, and can accurately predict the correspondence between the arch height and span of the interconnected wire as well as the insertion loss, return loss, and VSWR. The entire neural network model takes some time to train, and the neural network model can predict the data in a flash after completing the training, while the electromagnetic simulation takes a lot of time under the same conditions. The simulation results are not that much different from the prediction results. Additionally, this model can also study the microwave transmission characteristics under any parameters of the structure by just imputing the data into the neural network model we designed and the microwave transmission characteristics under the corresponding parameters will be obtained.  
Figure 11: Schematic diagram of VSWR neural network prediction results, they should be listed as: (**left**) The result of the first random draw. (**right**) The result of the second random selection.  
Figure 10: Insertion loss neural network prediction results schematic, they should be listed as: (**left**) The result of the first random draw. (**right**) The result of the second random selection.