Unidirectional scattering with spatial homogeneity using photonic time disorder
Jungmin Kim, Dayeong Lee, Sunkyu Yu, and Namkyoo Park

TL;DR
This paper explores the inverse design of photonic time disorder to achieve unidirectional scattering and bandwidth control in spatially homogeneous platforms, advancing optical functionalities without spatial patterning.
Contribution
It introduces a novel approach to engineer temporal disorder using structure factors and Green's functions, enabling controlled unidirectional scattering and bandwidth manipulation.
Findings
Engineered time scatterers enable unidirectional scattering.
Transition from order to disorder in time controls scattering bandwidths.
Resonance-free temporal color filtering achieved through temporal disorder manipulation.
Abstract
The temporal degree of freedom in photonics has been a recent research hotspot due to its analogy with spatial axes, causality, and open-system characteristics. In particular, the temporal analogues of photonic crystals have stimulated the design of momentum gaps and their extension to topological and non-Hermitian photonics. Although recent studies have also revealed the effect of broken discrete time-translational symmetry in view of the temporal analogy of spatial Anderson localization, the broad intermediate regime between time order and time uncorrelated disorder has not been examined. Here we investigate the inverse design of photonic time disorder to achieve optical functionalities in spatially homogeneous platforms. By developing the structure factor and order metric using causal Green's functions for the domain of time disorder, we demonstrate engineered time scatterer, which…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRandom lasers and scattering media · Neural Networks and Reservoir Computing · Advanced Optical Imaging Technologies
