Search NASA⌕ Search

DOE OSTI · 1735057

Low temperature cofired ceramic substrates and fabrication techniques for the same

Abstract

A low temperature cofired ceramic substrate comprises a plurality of dielectric layers, at least one inner conductor layer, a plurality of bond pads, and a solder mask. The dielectric layers are formed from ceramic material and placed one on top of another to form a stack. The inner conductor is formed from electrically conductive paste and positioned on an upper surface of at least one inner dielectric layer. The bond pads are positioned on an outer surface of the stack. Each bond pad is formed from a plurality of conductive sublayers of thin film metal stacked one on top of another, with each conductive sublayer being formed from a different metal. The solder mask is positioned on the same outer surface of the stack as the bond pads and includes a plurality of openings, with each opening exposing at least a portion of one of the bond pads.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Young, Barbara, Hamm, Randy. 2020-07-21. Low temperature cofired ceramic substrates and fabrication techniques for the same. https://www.osti.gov/biblio/1735057

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↗