Inverse design for enhanced chiroptical response with chiral nanophotonic structures

Chiral nanophotonic structures have attracted significant attention due to their wide-ranging applications in chiral sensing, on-chip optical devices, and optical communication. Although most chiral structures to date have been generated using conventional design approaches—guided by human intuition and followed by parameter sweeps—these methods are inherently limited in efficiently exploring complex, high-dimensional design spaces. Recently, inverse design methods have emerged to overcome traditional limitations by systematically and efficiently navigating vast parameter spaces to discover non-intuitive chiral geometries with enhanced performance. In this work, we present a range of chiral nanophotonic structures along with their corresponding design approaches, as well as performance metrics. This perspective focuses on inverse design methodologies, such as metaheuristic optimization, topology optimization, and data-driven approaches, tailored to the needs of enhanced chiroptical response. We also introduce conventional design strategies and compare them with optimization- and learning-based design pipelines. Particular emphasis is placed on the formulation of objective functions for circular dichroism and helical dichroism sensing, structural symmetry breaking, and computational constraints in structure design. We further highlight representative high-performance chiral metasurface and metamaterial designs developed using these strategies and conclude by outlining open challenges and future research directions.