Interconnection and packaging isses of microelectromechanical systems (MEMS) and COTS MEMS
MEMS quality and reliability challenges are discussed and needs for study in these areas are identified.
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MEMS quality and reliability challenges are discussed and needs for study in these areas are identified.
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Packaging techniques and systems design for applying solid-to-liquid phase change concept to spacecraft temperature control subsystems
Microelectromechanical systems (MEMS) have shown a significant promise in the last decade for a variety of applications such as air-bag, pressure sensors, accelerometer, microgyro, etc.
Many aspects of chip scale package (CSP) technology, with focus on assembly reliability characteristics, are being investigated by the JPL-led consortia. Three types of test vehicles were considered for evaluation and currently two configurations have been built to optimize attachment processes.
When the Crew Exploration Vehicle (CEV) is launched, the spacecraft adaptor (SA) fairings that cover the CEV service module (SM) are exposed to aero heating. Thermal analysis is performed to compute the fairing temperatures and to investigate whether the temperatures are within the material limits for nominal ascent aeroheating case. The ascent heating is analyzed by using computational fluid dynamics (CFD) and engineering codes at Marshall Space Flight Center. The aeroheating environment data used for this work is known as Thermal Environment 3 (TE3) heating data. One of the major concerns is with the SA fairings covering the CEV SM and the SM/crew launch vehicle (CLV) flange interface. The TE3 heating rate is a function of time, wall temperature, and the spatial locations. The implementation of the TE3 heating rate as boundary conditions in the thermal analysis becomes challenging. The ascent heating thermal analysis on SA fairings and SM/CLV flange interface are performed using two commercial software packages: Cullimore & Ring (C&R) Thermal Desktop (TD) 5.1 and MSC Patran 2007r1 b. TD is the pre-and post-processor for SINDA, which is a finite-difference-based solver. In TD, the geometry is built and meshed, the boundary conditions are defined, and then SINDA is used to compute temperatures. MSC Pthermal is a finite-element- based thermal solver. MSC Patran is the pre- and post-processor for Pthermal. Regarding the boundary conditions, the convection, contact resistance, and heat load can be imposed in different ways in both programs. These two software packages are used to build the thermal model for the same analysis to validate each other and show the differences in the modeling details.
Different aspects of advanced surface mount package technology have been investigated. Three key areas included the assembly reliability of conventional Surface Mount (SM), Ball Grid Arrays (BGAs), and Chip Scale Packages (CSPs).
NOTE: The manuscript associated with this data package is currently in review. The data/scripts may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final scripts and additional metadata. This data package is associated with the publication “Revisiting controls on hyporheic respiration with knowledge-guided machine learning at continental scale” submitted to Environmental Science & Technology (Zheng et al. 2026). The project combines mechanistic process modeling with knowledge-guided machine learning (KGML) to evaluate how organic matter chemistry, microbial biomass, and physical substrate accessibility regulate realized respiration rates across river corridors. All data used in this paper have been previously published and can be accessed at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719 (Goldman et al., 2020). This data package contains 3 R-markdown (Rmd) preprocessing scripts for the previously published data and subsequent modelling workflows. The full workflow with input and output data can be found in the associated GitHub repository at https://github.com/jianqiuz/KGML-WHONDRS.
This data set consists of results from soil nutrient profile, untargeted metabolomics, mass spec imaging, and amplicon sequencing. Data for soil nutrient profile includes common cations (Ca, Mg, Na, and K etc.) extracted from 3 digesting steps – ammonia acetate (for exchangeable cations), nitric acid (for acid dissolved fraction), and hydrofluoric acid/perchloric acid (HF/HClO4) for whole soil digestion. It also includes concentration of organic carbon, inorganic nitrogen (ammonia and nitrate) and phosphorus (Bray-1 P and nitric acid extract), and total nitrogen and phosphorus. Data for untargeted metabolomics includes metabolomic profile for root exudate/tissues and soil extracts from depths at surface soil to saprolite, that were measured using gas chromatography – mass spectrometry (GC-MS), and liquid chromatography – tandem mass spectrometry (LC-MS/MS). Data for mass spec imaging includes spatial distribution of metabolites that were detected and annotated with Fourier transformation ion cyclotron resonance mass spectrometer (FTICR-MS). Data for amplicon sequencing includes the base paired 16S and ITS ribosomal RNA sequences from Miseq Illumina sequencing. All samples were collected from 2 sampling campaign October 2022 and June 2023. Collectively, these datasets enable a mechanistic evaluation of how nutrient acquisition, especially nitrogen and phosphorus, differs between shallow roots operating in soil and deep roots functioning within the fractured bedrock zone. All files are provided as comma-separated values (CSV) fies (.csv) and (GZIP) file (.gz). The compressed .gz FASTQ files can be read directly in R using the dada2 package as part of the amplicon sequence analysis workflow. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research was performed on a project award 60563 (https://dx.doi.org/10.46936/expl.proj.2022.60563/60008727) from the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830.
A JPL-led chip scale package consortium of enterprises recently joined together to pool in-kind resources for developing the quality and reliability of chip scale packages for a variety of projects. The experience of the consortium in building more than 150 test vehicle assemblies, single and double sided multilayer PWBs, and the environmental test results has now been published as a chip scale package guidelines document.
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Explore the source record for details and available documents.
A JPL-led chip scale package (CSP) Consortium of enterprises, composed of representing agencies and private companies, recently joined together to pool in-kind resources for developing the quality and reliability of chip scale packages (CSPs) for a variety of projects.
A JPL-led chip scale package (CSP) Consortium, composed of team members representing government agencies and private companies, recently joined together to pool in-kind resources for developing the quality and reliability of chip scale packages (CSPs) for a variety of projects.
Reviews the many factors that affect interconnect reliability of emerging chip scale package (CSP) assemblies. These include: package type, package build, board design, and assembly variables.
The Mars exploration program of NASA and the international community will evolve from an early emphasis on orbital remote sensing toward in situ science activity on, or just above, the Martian surface.
This Guide explains how to create climate series and climate change scenarios by using the AgMip Climate team's methodology as outlined in the AgMIP Guide for Regional Assessment: Handbook of Methods and Procedures. It details how to: install R and the required packages to run the AgMIP Climate Scenario Generation scripts, and create climate scenarios from CMIP5 GCMs using a 30-year baseline daily weather dataset. The Guide also outlines a workflow that can be modified for application to your own climate data.