# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
## 3 Types of Chiral Metasurfaces
### Deep-Learning-Enabled Design of Chiral Metasurface  
Recently, a modeling procedure based on deep learning (DL) was introduced to shorten the time required for characterization while maintaining accuracy [78; 79; 80; 110; 144; 145]. Available computer simulation methods such as the finite-element method (FEM) are accurate but require considerable computational time. The CD value of the chiral metasurface depends on different parameters, as previously discussed, and the value of each parameter also determines the chiroptical response. Thus, it is vital to explore new uses of effective algorithms for effective and timely computation in computer modeling. However, due to the complexity of neural network models, DL-based chiral metasurface design often demands substantial computational resources, such as powerful TPUs or GPUs for efficient inference and training. Furthermore, access to massive datasets and software frameworks such as TensorFlow or PyTorch are required for effective implementation [78; 79; 80; 81]. Two significant examples of the DL-enabled design of chiral metasurfaces are presented in Figure 7.  
To analyze the CD signal of the chiral metasurface, a CNN model called CDCNN was presented [79]. The CD signal of the metasurface was considered as a regression problem. In  
Figure 7: (**a**) Principle of the DL-based prediction of spectral algorithm and (**b**) the architecture of the CDCNN network. (**c**) CD response with the ELU activation function. (**d**) CD response with the ReLU activation function ((**a–d**) reproduced from [79]). (**e**) Overview of the design procedure of the metasurface for the reflective spectra, including the EM numerical simulations, simulation for the forward prediction, as well as the application of neural network for the inverse design. (**f**,**g**) Anisotropic reflection phase response of the developed pattern along with predefined geometric data, where (**f**) is the test sample of the sector and (**g**) is the modified handwritten MNIST test sample digit. The dots represent the simulated results of the developed patterns ((**e–g**) reproduced from [81]). addition, DL for CD spectral prediction was used rather than solving Maxwell's equations by training with data from COMSOL Multiphysics simulations, as illustrated in Figure 7a. The dataset used to train the CDCNN model consists of the images of the metasurface and their CD spectra with 41 data points in the 400-1000 nm wavelength range. It began with 200+ basic L-shaped nanohole structures and was later enlarged to 1000+ structures through mirroring and rotations for better deep-learning performance [79]. The purpose of the convolution layer was to extract the structural features of the metasurface data associated with the CD response. As shown in Figure 7b, the CDCNN employs several sequential conventional layers. Activation functions like ELU and ReLU are important in CDCNN models because they introduce nonlinearity into the neural network. They allow the neural network to recognize complicated correlations in data and make precise predictions. In this study, ELU outperformed ReLU because it handles negative inputs more effectively [79]. CD spectra data can contain negative values, and utilizing ReLU as an activation function would result in the loss of essential information by forcing all negative CD values to zero during training. ELU, on the other hand, enables a smooth transition between negative and positive inputs, preserving the data's characteristics and resulting in more accurate CD spectra predictions [79]. The predicted CD response with the activation functions of rectified linear unit (ReLU) and exponential linear unit (ELU) fits real value, with ELU fitting better than ReLU, as depicted in Figure 7c,d. Compared with traditional finite-element approaches, it is a thousand times faster to properly map chiral metasurfaces and forecast their optical responses [79].