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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Physics of failure analysis of Xilinx Flip Chip CCGA packages: effects of mission environments on properties of LP2 underfill and ATI lid adhesive materials

The Xilinx Virtex 4QV and 5QV (V4 and V5) are next-generation field-programmable gate arrays (FPGAs) for space applications. However, there have been concerns within the space community regarding the non-hermeticity of V4/V5 packages; polymeric materials such as the underfill and lid adhesive will be directly exposed to the space environment. In this study, reliability concerns associated with the non-hermeticity of V4/V5 packages were investigated by studying properties and behavior of the underfill and the lid adhesvie materials used in V4/V5 packages.

Suh, Jong-ook↗
NASA NTRS2022

Radiation Effects Characterization and System Architecture Options for the 7nm Snapdragon SA8155P Automotive Grade System on Chip (SoC)

The SA8155P SoC is a complex heterogeneous computational platform with CPUs, GPU, DSPs and NPU. The SA8155P is a 7 TOPS/7 Watts capable AI/ML platform and as such represents a game changing breakthrough in terms computational power for space applications. We characterize the TID and SEE effects of the Qualcomm SA8155P Automotive grade SoC. Individual sub-system testing as well as application specific full device testing was conducted. We also discuss the commercial and automotive recovery mechanisms that are available with the SA8155P SoC generation and how those can form the basis for resilient architecture options for use in future space missions

Allen, Greg↗
NASA NTRS2025

System for Long-Duration Electrical Testing of SiC IC Generation 12 Chips at 500 °C

The system described in this report is an improved testing rig designed to enhance measurement flexibility, provide a compact oven footprint, and reduce costs for parallel oven-testing silicon carbide (SiC) integrated circuit devices at 500 °C for long durations (greater than 100 h). The testing rig design was characterized in terms of the ovens’ internal and external temperatures, system noise, and the leakage properties of their substrate/inner boards, where the devices under test (DUTs) reside. The test rig employs a National Instruments (NI) Peripheral Component Interconnect eXtensions for Instrumentation (PXI) measurement system, room-temperature pin-switching/customizing boards, and high-temperature compact ovens.

Stephanie Booth↗
DOE OSTI2024

Temperature-tuned ultrafast X-ray shutter using optics-on-a-chip

Typically modulation systems are incapable of performing synchronous modulation for high-energy radiation systems. A method and system for performing high-energy synchronous radiation modulating is described. The method includes providing an oscillatory diffractive element, with the oscillatory diffractive element capable of being oscillated over a range of angles. A radiation source provides radiation to the oscillatory diffractive element. An electrical signal is provided to electrodes that oscillate the oscillatory diffractive element to modulate the radiation. A temperature controller controls the temperature of the oscillatory diffractive element to tune the oscillatory motion of the oscillatory diffractive element.

Wang, Jin↗
DOE OSTI2026

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

Das, Arghya Ranjan [Purdue U.] (ORCID:000000018451↗