Search NASA⌕ Search

DOE OSTI · 1083225

Method for fabricating a microelectromechanical resonator

Abstract

A method is disclosed which calculates dimensions for a MEM resonator in terms of integer multiples of a grid width G for reticles used to fabricate the resonator, including an actual sub-width L.sub.a=NG and an effective electrode width W.sub.e=MG where N and M are integers which minimize a frequency error f.sub.e=f.sub.d-f.sub.a between a desired resonant frequency f.sub.d and an actual resonant frequency f.sub.a. The method can also be used to calculate an overall width W.sub.o for the MEM resonator, and an effective electrode length L.sub.e which provides a desired motional impedance for the MEM resonator. The MEM resonator can then be fabricated using these values for L.sub.a, W.sub.e, W.sub.o and L.sub.e. The method can also be applied to a number j of MEM resonators formed on a common substrate.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wojciechowski, Kenneth E, Olsson, III, Roy H. 2013-02-05. Method for fabricating a microelectromechanical resonator. https://www.osti.gov/biblio/1083225

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↗