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Terahertz high-quality-factor topological corner-state cavity designed via policy-based reinforcement learning

Oct 2026 · Physica Scripta
Topological Materials and Phenomena

Abstract

Abstract A single-diagonal breathing square-lattice second-order photonic topological insulator is studied, in which bulk-polarization calculations reveal that tuning the position of a single-diagonal pair of the ceramic rods within the unit cell can induce a topological phase transition. Through numerical analysis of topological edge and corner states located inside multiple bandgaps, a terahertz topological corner-state cavity is designed. Deep reinforcement learning method based on proximal policy optimization is employed to optimize the key performances of the corner-state cavity. The calculation results show that the proposed method can effectively enhance the quality factor of the corner state as well as reducing its mode volume. Our work may highlight the potential of the policy-based reinforcement learning method in efficient design and optimization of topological and metamaterial devices.

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