Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1735148

Circuit board interconnect decals

Hatch, Stephen McGarry·Hatch, Jonathan Douglas
View original source ↗Read source document ↗Export metadata ↓

Abstract

A circuit component decal comprising a transparent sheet and an opaque circuit pattern. The transparent sheet includes opposing top and bottom surfaces and a number of edges. The opaque circuit pattern includes an electronic component footprint and a number of circuit lead paths. The electronic component footprint includes a number of contact points representing the location of leads of the electronic component. The circuit lead paths extend from the contact points to the edges of the transparent sheet. The opaque circuit pattern corresponds to only a section of a complete circuit pattern and is configured to block energy from reaching a first portion of the intermediate substrate when the transparent sheet is positioned on the intermediate substrate so as to form the section of the complete circuit pattern.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hatch, Stephen McGarry, Hatch, Jonathan Douglas. 2020-08-11. Circuit board interconnect decals. https://www.osti.gov/biblio/1735148

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

KEEP EXPLORING

Related reports

DOE OSTI2026

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↗