Search NASA⌕ Search

DOE OSTI · 1901613

Selective thermal neutron transmutation doping with Gd masks in GaN semiconductors

Abstract

For the first time, selective neutron transmutation doping was successfully performed on GaN through Gd masks, showing the feasibility of patternable doping through neutron irradiations. In collaborating with Oak Ridge National Laboratory, GaN with Gd masks was irradiated by directional neutrons. By using the Gd properties with high neutron absorption cross-sections, it was confirmed that the area covered by Gd masks on GaN was completely shielded from neutrons, and the uncovered area was doped with the generated Ge and 14 C. Property differences were clearly identified in various ways when comparing the area exposed by neutrons and the area unexposed by neutrons. The discoloration by the ejected protons and elastic scattering of neutrons appeared only in the exposed area while the unexposed area remained the same as before irradiation. The aspect ratio of the patterned profile of GaN was estimated by comparing patterns on the front and backside. From energy dispersive X-ray spectroscopy spectra and secondary ion mass spectrometry measurement, the concentration difference between Ge and 14 C as well as the exact concentration of produced 14 C was also checked. Further, Schottky barrier diodes on the selectively doped GaN were fabricated and investigated to study the electrical properties.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kim, Jeongwoo, Frontzek, Matthias D., Crow. Jr., Lowell, Kwon, Jae W.. 2022-11-01. Selective thermal neutron transmutation doping with Gd masks in GaN semiconductors. https://doi.org/10.1016/j.sse.2022.108510

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↗