Search NASA⌕ Search

DOE OSTI · 1650773

Kinematic chip to chip bonding

Abstract

The various technologies presented herein relate to integrating an IC having at least one waveguide incorporated therein with a v-groove array IC such that an optical fiber located in a v-groove is aligned relative to a waveguide in the IC maximizing optical coupling between the fiber and the waveguide. The waveguide IC and the v-groove array IC are bonded in a stacked configuration. Alignment of the waveguide IC and the array IC in the stacked configuration enables advantage to be taken of lithographic accuracy of features formed with respect to the Z-direction. Further, kinematic pins and sockets are utilized to provision accuracy in the X- and Z-directions, wherein advantage is taken of the placement accuracy and fabrication tolerance(s) which can be utilized when forming the and sockets. Accordingly, automated alignment of the waveguide IC and the array IC is enabled, facilitating accurate alignment of the respective waveguides and fibers.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Burckel, David Bruce, Bauer, Todd, Henry, Michael David, Pomerene, Andrew T. S.. 2020-04-14. Kinematic chip to chip bonding. https://www.osti.gov/biblio/1650773

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↗