Engineering Papers⌕ Search

DOE OSTI · 2482429

High Linearity RF Circuits in CMOS Platforms (CRADA Final Report)

Abstract

The goal of this CRADA was to understand high linearity silicon-on-insulator (SOI) radio frequency switches. The first direction investigated was high linearity RF mixers as these components are composed of a network of switches. During the course of the investigation, it was determined that while the SOI RF switches were effective in the application in mixers and produced state of the art performance, the use of SOI for mixers is not economically feasible because the technology only worked with advanced CMOS nodes <65nm which can have an NRE cost (from lithography masks) in excess of $\$$500,000.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sonsteng, Melanie [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)], Hill, Cameron [Lintrinsic Semiconductors Inc., Sommerville, MA (United States)]. 2024-12-10. High Linearity RF Circuits in CMOS Platforms (CRADA Final Report). https://doi.org/10.2172/2482429

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↗