Engineering Papers⌕ Search

DOE OSTI · 1905284

Dynamically tunable second-harmonic generation using hybrid nanostructures incorporating phase-change chalcogenides

Abstract

Nonlinear metasurfaces with high conversion efficiencies have been vastly investigated. However, strong dynamic tunability of such devices is limited in conventional passive plasmonic and dielectric material platforms. Germanium antimony telluride (GST) is a promising phase-change chalcogenide for the reconfiguration of metamaterials due to strong nonvolatile changes of the real and imaginary parts of the refraction index through amorphous-crystalline phase change. The orderly structured GST has an even higher potential in tunable second-harmonic generation (SHG) with a non-centrosymmetric crystal structure at the crystalline phase, while the amorphous phase of GST does not exhibit bulk second-order nonlinearity. Here, we experimentally demonstrate SHG switches by actively controlling the crystalline phase of GST for a GST-based hybrid metasurface featuring a gap-surface plasmon resonance, and a quarter-wave asymmetric Fabry–Perot (F–P) cavity incorporating GST. We obtain SHG switches with modulation depths as high as ~ 20 dB for the wavelengths at the on-state resonance. We also demonstrate the feasibility of multi-level SHG modulation by leveraging three controlled GST phases, i.e., amorphous, semi-crystalline, and crystalline, for the gap-surface plasmon hybrid device, which features stronger light–matter interaction and has higher resonant SHG efficiencies than the asymmetric F–P cavity device at respective GST phases. This research reveals that GST-based dynamic SHG switches can be potentially employed in practical applications, such as microscopy, optical communication, and photonic computing in the nonlinear regime.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhu, Muliang, Abdollahramezani, Sajjad, Li, Chentao, Fan, Tianren, Harutyunyan, Hayk, Adibi, Ali. 2022-04-26. Dynamically tunable second-harmonic generation using hybrid nanostructures incorporating phase-change chalcogenides. https://doi.org/10.1515/nanoph-2022-0051

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗