# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
## 1 Introduction
Recent studies have shown that chiroptical responses such as CD and OR can be tuned using tunable chiral metasurfaces [7; 8; 9; 10; 11; 58; 61; 62; 63; 64; 65; 66; 67; 74; 75; 76; 77]. The active modification of chiroptical responses in chiral metasurfaces is attained by external stimuli like electrical fields, magnetic fields, thermal change, or incident light, which changes the properties of individual atoms [58; 66]. These changes can be controlled by integrating responsive materials, such as liquid crystals or phase-change materials, into the chiral metasurface design or by using dynamic modulation methods, such as optical pumping or voltage tuning, to attain real-time manipulation over the chiroptical properties [24; 60; 75]. The computation time required to characterize the chiroptical responses in numerical studies is substantial; thus, some researchers have introduced deep-learning-enabled chiral metasurfaces to improve the computation time and effectively characterize the chiroptical response of chiral structures [78; 79; 80; 81]. Researchers have been successfully employing deep learning in chiral metasurface design by training neural networks, like generative adversarial networks (GANs) and convolutional neural networks (CNNs) using extensive datasets containing chiral metasurface designs or predict chiroptical responses with high speed and accuracy, facilitating the efficient exploration of intricate parameter spaces and overcoming the limitations of traditional design procedures. The advantages of this method include quicker design refinements, improved performance, and the ability to discover novel chiral metasurface configurations for specific applications [79; 80; 81]. There are several challenges in the fabrication of chiral metasurfaces that researchers must address for practical applications [82; 83]. Achieving broadband performance across a wide range of wavelengths remains a challenge, as it often requires precise control over the shape and size of meta-atoms. Scalability to large areas while maintaining structural integrity is another challenge, and addressing material constraints, especially for nonstandard operating conditions, is crucial for the practical deployment of chiral metasurfaces [83; 84; 85; 86]. Herein, we review the fundamentals and different types of chiral metasurfaces. First, the fundamentals of chiral metasurfaces are introduced. These include the optical response, transmittance analysis for different symmetries of chiral structures, and assessment of CD. Second, we introduce different types of chiral metasurfaces. This section discusses single-layered chiral metasurfaces composed of arrays of 2D meta-atoms [87, 88] and multi-layered chiral metasurfaces. Multi-layered chiral metasurfaces allow for a greater degree of freedom in manipulating waves to achieve desirable chiral optical responses. In addition, we review some recently developed tunable chiral metasurfaces [89, 90, 91, 74, 75, 76, 77, 89, 91] that enable active EM wave control. Finally, we cover some of the deep-learning-enabled designs of chiral metasurfaces that reduce the amount of time required for computer simulations [78, 79, 80, 81].