## 3 Analysis and Evaluation of Readout Information
### Readout Result Based on Spectrum Response  
To prepare an appropriate ANN training sequence, 300 spectra were randomly generated for 120 structures, when the wavelength was between 200 nm and 1000 nm, within a 20% noise level of the initial spectrum [33]. Out of the 20% spectral noise considered in this research, 10% was used to simulate errors due to manufacturing, and the remaining 10% was used to simulate noise based on measurement. Both manufacturing error and measurement error have uncontrollable factors that will affect the actual readout accuracy. The manufacturing noise and measurement noise vary according to the actual measurement equipment and environment. According to the research of [PERSON]. [PERSON] et al. [16], it is reasonable to set the manufacturing noise and measurement noise at 10%, respectively. Figure 8a shows the cross-entropy loss corresponding to the 12, 18, and 120 data sets at different polarizations. Figure 8b shows the validation accuracy corresponding to the 12, 18, and 120 data sets at different polarizations. With the increase in epoch, the training using _TE_ polarization converged faster than that using _TM_ polarization. As shown in Figure 8a,b, in the training of 12 data sets using _TE_ polarization, the validation accuracy at the 10 th epoch was 100%, with a corresponding loss of 0.0036. Similarly, in the training of 18 data sets using _TE_ polarization, the validation accuracy was 100% with a relative loss of 0.0017.  
Figure 7: Schematic diagram of a convolutional ANN. The spectra corresponding to the different sloppy structures are used as inputs to ANN; the encoded data information is used as the ANN output.  
The validation accuracy reaches 99.86% in the epoch, with a loss of 0.00022 in training with 120 data sequences. It can be clearly seen that the convergence rate gradually slows down as the number of data sets increases, but the verification accuracy undergoes only minor changes. This may be due to the appropriate selection of steps in our encoding strategy. If the step chosen is too small, the verification accuracy may be greatly reduced. For example, in the data encoding pattern \"\(\alpha\)\(\psi\)\" in this study, the step of \(\alpha\) and \(\psi\) is set to 3.75\({}^{\circ}\) and 10\({}^{\circ}\), respectively, which ensures enough encoded data sequences without affecting the quality of training. In the case that more coding data sequences are needed in future work, a smaller step can be fixed.  
The t-distributed stochastic neighbor embedding (t-SNE) was used to assess the uniqueness of different structures and analyze data sets [34]. In the t-SNE scatter plot, well-distributed scattered spots represented identifiable structures based on data sets, but neighboring and overlapping spots implied that the data had some similarity. Figure 8c shows the results for 12, 18, and 120 data sets at _TE_ and _TM_ polarization. As shown in the figure, the error was set to 20%. Different colors are matched to different structures, while each spot represents a simulated spectrum. In the figure of t-SNE, the data sets of each structure are well distinguished, which explains the ability of ANN to correctly recognize information on data with 100% accuracy.  
Figure 8: Demonstration of training quality and readout accuracy. (**a**) Cross-entropy loss for 12, 18, and 120 data sequences trained using _TE_ polarization and _TM_ polarization, (**b**) Validation accuracy for 12, 18, and 120 data sequences trained using _TE_ polarization and _TM_ polarization, (**c**) 12, 18, and 120 data sets of _TE_ polarized training sets with a t-SNE spot.