Quantum annealing-aided design of an ultrathin-metamaterial optical diode
- University of Notre Dame, IN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)
- Kyung Hee Univ., Seoul (Korea, Republic of)
- University of Notre Dame, IN (United States)
Thin-film optical diodes are important elements for miniaturizing photonic systems. However, the design of optical diodes relies on empirical and heuristic approaches. This poses a significant challenge for identifying optimal structural models of optical diodes at given wavelengths. Here, we leverage a quantum annealing-enhanced active learning scheme to automatically identify optimal designs of 130 nm-thick optical diodes. An optical diode is a stratified volume diffractive film discretized into rectangular pixels, where each pixel is assigned to either a metal or dielectric. The proposed scheme identifies the optimal material states of each pixel, maximizing the quality of optical isolation at given wavelengths. Consequently, we successfully identify optimal structures at three specific wavelengths (600, 800, and 1000 nm). In the best-case scenario, when the forward transmissivity is 85%, the backward transmissivity is 0.1%. Electromagnetic field profiles reveal that the designed diode strongly supports surface plasmons coupled across counterintuitive metal–dielectric pixel arrays. Thereby, it yields the transmission of first-order diffracted light with a high amplitude. In contrast, backward transmission has decoupled surface plasmons that redirect Poynting vectors back to the incident medium, resulting in near attenuation of its transmission. In addition, we experimentally verify the optical isolation function of the optical diode.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Organization:
- USDOE Office of Science (SC); National Research Foundation of Korea (NRF); USDOE
- Grant/Contract Number:
- AC05-00OR22725; RS-2023-00255442; RS-2023-00207966
- OSTI ID:
- 2349157
- Alternate ID(s):
- OSTI ID: 2351053
- Journal Information:
- Nano Convergence, Vol. 11, Issue 1; ISSN 2196-5404
- Publisher:
- Springer NatureCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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