Search NASA⌕ Search

DOE OSTI · 2287695

Arbitrary electro-optic bandwidth and frequency control in lithium niobate optical resonators

Abstract

In situ tunable photonic filters and memories are important for emerging quantum and classical optics technologies. However, most photonic devices have fixed resonances and bandwidths determined at the time of fabrication. Here we present an in situ tunable optical resonator on thin-film lithium niobate. By leveraging the linear electro-optic effect, we demonstrate widely tunable control over resonator frequency and bandwidth on two different devices. We observe up to ∼50 × tuning in the bandwidth over ∼50 V with linear frequency control of ∼230 MHz/V. We also develop a closed-form model predicting the tuning behavior of the device. This paves the way for rapid phase and amplitude control over light transmitted through our device.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Herrmann, Jason F. (ORCID:0000000245835496), Dean, Devin J., Sarabalis, Christopher J. (ORCID:0000000335707391), Ansari, Vahid (ORCID:0000000240200627), Multani, Kevin, Wollack, E. Alex, McKenna, Timothy P. (ORCID:0000000181327336), Witmer, Jeremy D., Safavi-Naeini, Amir H. (ORCID:0000000161761274). 2024-02-06. Arbitrary electro-optic bandwidth and frequency control in lithium niobate optical resonators. https://doi.org/10.1364/oe.502142

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↗