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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An introduction to neuromorphic computing and its potential impact for unattended ground sensors

Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Two 28-nm front-end ASICs for ultra-fine spatial resolution and precision timing to be 3D integrated with 12 LGADs

The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (about 10 µm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present two 28 nm CMOS ASIC prototypes, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.

England, Troy [Fermilab] (ORCID:0000000154405255)↗
DOE OSTI2026

Two 28-nm front-end ASICs for ultra-fine spatial resolution and precision timing to be 3D integrated with 12 LGADs

The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (about 10 µm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present two 28 nm CMOS ASIC prototypes, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.

England, Troy [Fermilab] (ORCID:0000000154405255)↗
DOE OSTI2023

Harmonic Signals Dataset

The Harmonic Signals Dataset (HSD) consists of one-dimensional time series data collected from current sensors with a goal of providing data for research and development of Non-Intrusive Load Monitoring (NILM) with high sample rate (800kHz) sensors. NILM seeks to detect and characterize electrical equipment operating within a facility by sensing changes in electrical current on the building power system associated with the equipment. To provide data with a known ground truth, but with the realism of a signal introduced within a building facility, a series of known ground truth signals were injected as voltage into the power system of a building and measurements of the signals at multiple locations across the building power system were made with electrical current sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

PERFORMANCE CHARACTERISTICS OF A VECTOR SENSOR ARRAY IN AN ENERGETIC TIDAL CHANNEL

A compact three-dimensional vector sensor array (termed the NoiseSpotter) was recently developed with the goal of characterizing and localizing sources of sound in energetic environments such as those where marine renewable energy devices are likely to be located. The three vector sensors on the NoiseSpotter are mounted on a bottom platform with each sensor suspended inside an optional flow noise removal shield. The multiple pressure and particle velocity channels on the NoiseSpotter are simultaneously sampled and stored on-board to enable coherent array processing of vector sensor array data. A series of field trials were conducted where the NoiseSpotter was deployed on a bottom platform in an 8 m deep tidal channel in Sequim Bay, Washington. Pure tones were transmitted from a drifting vessel whose distance from the bottom platform varied between 50 m and 500 m. The effect of flow noise on vector sensor array performance is quantified along with the detectability of low intensity sound in an energetic environment. Flow noise removal efficiency for each channel of every vector sensor is demonstrated using multiple metrics derived from the active and reactive intensities computed using the pressure-velocity cross-spectral density matrix. A flow noise removal shield is shown to improve the detectability of low intensity sound such as those that are expected to be emitted by marine renewable energy devices.

Raghukumar, Kaustubha↗
DOE OSTI2023

Methodology to Account for Radiation and Temperature Influences on Optical Fibers compaction and Index of Refraction

The effects of radiation induced compaction and index of refraction change can cause significant drift in the measurements from intrinsic fiber optic temperature sensors. The work presented here aims to develop and test a methodology to measure these macroscopic properties that are responsible for the drift in order to correct the measurement. This report discusses the measurement methodology, theory, and sensor construction. Preliminary results from the first irradiation test using this sensor are provided here.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Evaluation of the Reliability of Passive Infrared (PIR) Occupancy Sensors for Residential Indoor Lighting

Solid-state lighting (SSL) technologies have penetrated the general illumination market in recent years, largely replacing conventional technologies such as incandescent and fluorescent lighting. Most of the initial excitement about light-emitting diode (LED) sources for SSL devices focused on their energy savings potential resulting from vast improvements in source efficiency and luminous efficacy compared with conventional illumination products. More recently, the focus has shifted toward other aspects of lighting application efficiency, namely intensity effectiveness and spectral efficiency, because of the ease of controlling both the light intensity of LEDs with drive voltage and the color properties of the LED-based illuminators. To capitalize on the energy savings of increased intensity effectiveness and spectral efficiency, a lighting control system (LCS) is often used. While the current penetration of LCSs is relatively modest, it is anticipated that lighting controls (i.e., connected lighting, controls and LED and conventional lighting) could have an installed penetration as higher as 46% by the year 2035, saving an additional 1.3 quads of energy. Despite the large energy savings that can be gained from using LCSs, standard test methods for evaluating sensors employed in LCSs and reliability data of the LCSs and their components are generally lacking. The National Electrical Manufacturers Association (NEMA) developed the only standard, NEMA WD 7-2011 (R2016), to test occupancy and motion sensor performance (herein referred to as “the NEMA protocol”). In a previous U.S. Department of Energy (DOE) effort, some concerns of the NEMA protocol were identified (e.g., strict height and weight limits on test subjects, large amounts of manual effort, sometimes inconsistent repeatability). During the current work, a consistent detection length test (DLT) and a Robotic Sensor Evaluation System (RoboSES) were developed to alleviate some of these concerns. RoboSES acts as a human surrogate by using thermal pads on a mannequin and a remote-controlled mobile base to test a sensor’s field of view (FOV). This report builds on the earlier DOE efforts to understand sensor technologies used in LCSs for general illumination. Specifically, this report describes the optimization of RoboSES, characterizes and establishes test methods to assess the reliability of multiple passive infrared (PIR) sensors, and reports the findings of robustness and reliability testing on two commercial PIR sensors intended for residential applications. The information presented in this report is gained from up to 4,000 hours (hrs) of accelerated stress tests (ASTs).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Spatiotemporal flame propagations, combustion and solid particle emissions from lean and stoichiometric gasoline direct injection engine operation

Increased particle number and mass emissions in gasoline direct injection (GDI) engines should require to investigate in-cylinder flame and combustion characteristics associated with primary source of particle emissions. In this article, in-cylinder spatiotemporal flame luminosity is quantitatively characterized to features combustion process and solid particle emissions from a GDI engine operating in two lean and one stoichiometric modes. Low- and high-steady state operating points were used to compare combustion strategies on flame development and emission characteristics. A fiber-optic sensor composed of eight measurement channels detected the flame front and the direction of the flame propagation in the combustion chamber. Solid particle emissions in the exhaust were measured using an engine exhaust particle sizer and a micro soot sensor. Results of the experiments showed that two lean combustion modes by injection strategies resulted in distinct combustion and flame development. Lean combustion modes generated high diffusion flame by burning stratified rich-mixture. Although the lean cases resulted in strong diffusion flames, the lean-homogeneous produced similar particle size distributions with the stoichiometric mode with high ash particles. Piston pool fires on the piston surface in the lean-stratified mode induced a large accumulation mode with high particle mass concentrations.

42 ENGINEERING↗
DOE OSTI2019

Resilient Reinforcement in Secure State Estimation against Sensor Attacks with a priori Information

This paper deals with the state estimation problem in the presence of malicious sensor attacks. Unlike the existing work, in this paper, we address the problem with a priori information of the state to be estimated. Specifically, we address three prior knowledge: the sparsity information, (alpha, n_0)-sparsity information, and side information, and in each scenario, we show that the state can be reconstructed even if more sensors are compromised. This implies that the prior information reinforces the system resilience against sensor attacks. Then, an estimator under sensor attacks considering the information is developed and, under a certain condition, the estimator can be relaxed into a tractable convex optimization problem. Further, we extend this analysis to systems in presence of measurement noises, and it is shown that the prior information reduces the state-estimation error caused by the noise. Here, the numerical simulations in a diffusion process finally illustrate the reinforcement and error-reduction results with the information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Infrared Cloud Imager Instrument Intercomparison Report

The Infrared Cloud Imager Instrument Intercomparison was a guest instrument deployment by NWB Sensors to the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility observatory on the Southern Great Plains (SGP) between May 18 and December 12, 2023. NWB Sensors is a company that has developed a commercially available infrared cloud imager (ICI). The ICI provides radiometrically calibrated, full-sky images of the downwelling infrared radiance in the 7.3-14 µm band. In addition, it provides cloud radiance as the residual between the observed radiance and the modeled cloud-free radiance as well as derived cloud products. The instrument is used in applications that require consistent detection of clouds across day and night. For more information, consult the instrument's webpage. The primary goal of the deployment was to validate the radiometric accuracy of the ICI. The ICI uses a proprietary calibration method to convert the raw data from its infrared camera into downwelling radiance. Unlike similar instruments, the system does not have an onboard blackbody calibration standard. Instead, NWB Sensors characterizes each ICI camera individually in an environmental chamber while looking at a blackbody standard. The resulting (proprietary) calibration is used operationally in the instrument and has been demonstrated to be stable over long periods. To validate the radiometric products from the ICI, an intercomparison between the ICI data products and those from ARM’s atmospheric emitted radiance interferometer (AERI) was made. The AERI is a best-in-class instrument for measuring downwelling infrared radiance (Gero et al. 2025). A weighted integration of the AERI’s spectral radiances across the ICI’s camera response was performed. The resulting radiance (herein called the AERI radiance) was directly compared to the zenith radiance concurrently observed by the ICI. The results of these comparisons are reported in the next section of this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Combinatorial Sensor Design in Caulobacter crescentus for Selective Environmental Uranium Detection

The ability to detect uranium (U) through environmental monitoring is of critical importance for informing water resource protection and nonproliferation efforts. Although technologies exist for environmental U detection, wide-area environmental monitoring, i.e. sampling coverage over large areas not known to possess U contamination, remains a challenging prospect that necessitates the development of novel detection approaches. In this work, we describe the development of a whole-cell U sensor by integrating two functionally independent, native U-responsive two-component signaling systems (TCS), UzcRS and UrpRS, within an AND gate circuit in the bacterium Caulobacter crescentus. Through leverage of the distinct but imperfect selectivity profiles of both TCS, this combinatorial approach enabled greater selectivity relative to a prior biosensor developed with UzcRS alone; no cross-reactivity was observed with most common environmental metals (e.g, Fe, As, Cu, Ca, Mg, Cd, Cr, Al) or the U decay-chain product Th, and the selectivity against Zn and Pb was significantly improved. In addition, integration of the UzcRS signal amplifier protein UzcY within the AND gate circuit further enhanced overall sensitivity and selectivity for U. The functionality of the sensor in an environmental context was confirmed by detection of U concentrations as low as 1 μM in groundwater samples. The results illustrate the value of a combinatorial approach for constructing whole-cell sensors for the selective detection of analytes for which there are no known evolved regulators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Temperature Field Reconstruction of Surfaces Heated Through Radiative Heat Transfer Using Convolutional Neural Networks

Microreactors could play a crucial role in decarbonizing our energy portfolio. However, their development and implementation come with specific challenges, particularly regarding cost. Due to their compact size and the harsh operational environment, collecting real-time data on reactor operation can be challenging. Many probe designs are unable to withstand extreme conditions (e.g., temperature, radiation) in the reactor. In this context, using convolutional neural networks (CNNs) can pave the way for developing a nonintrusive approach that relies solely on ex-core sensors. A well-trained physics-informed CNN can reconstruct the distribution of a given physical quantity over a domain using only a few sensors, allowing us to reconstruct the desired field distribution even in a limited space or complex geometries where a large array of sensors is impractical. In this work, we present the initial steps toward developing a real-time tool for monitoring the thermal behavior of nuclear reactor pressure vessels. Based on an experimental setup, a computational model using the Multiphysics Object-Oriented Simulation Environment (moose) framework was built, where the Ray Tracing and Heat Conduction modules were used to evaluate the temperature distribution over a convex metal surface heated through radiative heat transfer. This metal surface represents a section of a heated nuclear reactor vessel wall. The model also accounts for solid mechanics physics through the moose Solid Mechanics module. In situ experimental data, acquired from a Texas A&M facility, were used to validate the computational model. Part of the data generated by the moose model was used to train the convolutional neural network to reconstruct the vessel wall's outer surface temperature. The CNN generalization was then compared against the experimental and computational data.

Aldeia Machado, Luiz Carlos↗
DOE OSTI2024

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗
DOE OSTI2023

Rational design of a genetically encoded NMR zinc sensor

Elucidating the biochemical roles of the essential metal ion, Zn 2+ , motivates detection strategies that are sensitive, selective, quantitative, and minimally invasive in living systems. Fluorescent probes have identified Zn 2+ in cells but complementary approaches employing nuclear magnetic resonance (NMR) are lacking. Recent studies of maltose binding protein (MBP) using ultrasensitive 129 Xe NMR spectroscopy identified a switchable salt bridge which causes slow xenon exchange and elicits strong hyperpolarized 129 Xe chemical exchange saturation transfer (hyper-CEST) NMR contrast. To engineer the first genetically encoded, NMR-active sensor for Zn 2+ , we converted the MBP salt bridge into a Zn 2+ binding site, while preserving the specific xenon binding cavity. The zinc sensor (ZS) at only 1 μM achieved ‘turn-on’ detection of Zn 2+ with pronounced hyper-CEST contrast. This made it possible to determine different Zn 2+ levels in a biological fluid via hyper-CEST. ZS was responsive to low-micromolar Zn 2+ , only modestly responsive to Cu 2+ , and nonresponsive to other biologically important metal ions, according to hyper-CEST NMR spectroscopy and isothermal titration calorimetry (ITC). Protein X-ray crystallography confirmed the identity of the bound Zn 2+ ion using anomalous scattering: Zn 2+ was coordinated with two histidine side chains and three water molecules. Penta-coordinate Zn 2+ forms a hydrogen-bond-mediated gate that controls the Xe exchange rate. Metal ion binding affinity, 129 Xe NMR chemical shift, and exchange rate are tunable parameters via protein engineering, which highlights the potential to develop proteins as selective metal ion sensors for NMR spectroscopy and imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Abstract for CRADA between NETL and Louisiana State University

The National Energy Technology Laboratory (NETL) and Louisiana State University (PARTICIPANT) will collaborate in developing and testing distributed optical fiber CO 2 sensors for CO 2 storage site monitoring. Safe and reliable monitoring of CO 2 storage sites and transport infrastructure is critical for ensuring the long-term effectiveness and integrity of carbon capture and storage (CCS). In this project, the objective is to develop a novel optical fiber-based distributed CO 2 sensor for long-term monitoring of CCS sites and CO 2 pipelines and also implement machine learning to automate leak and structural anomaly detection. This effort aligns with NETL’s mission in reliable and sustainable energy and decarbonization.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Functionalization of nitrogen vacancy-containing nanodiamonds with a metal-organic framework for quantum sensing applications

Nitrogen vacancy (NV)-containing nanodiamonds (NDs) are an important material in applications such as biological imaging, catalysis, and, in particular, quantum sensing. Careful manipulation of the surface coating on NV NDs is essential for both enhancing quantum sensor performance and for tuning selectivity towards specific sensing targets. Here, we demonstrate a simple synthetic approach for functionalizing NV NDs with the zeolitic imidazole framework-8 (ZIF-8) metal–organic framework (MOF), providing a well-ordered, porous scaffold for immobilizing target analytes near the NV ND surface. The composites were structurally characterized by x-ray diffraction, electron microscopy, and X-ray photoelectron spectroscopy, and these results were all consistent with NV NDs fully encapsulated by ZIF-8. Critically, the luminescent properties of the NV NDs, which are vital for quantum sensing experiments such as optically detected magnetic resonance (ODMR), are unchanged by the MOF coating. Moreover, spin relaxometry experiments indicate that the ZIF-8 coating significantly enhances the NV ND spin longitudinal relaxation time T1, a critical quantum parameter for sensing applications. Given the tremendous structural diversity of MOFs, the NV ND@MOF composites are an exciting material class with exciting implications for the development of high-performance quantum sensors.

Crawford, Scott↗
DOE OSTI2020

Commercially Fabricated Antenna-Coupled Transition Edge Sensor Bolometer Detectors for Next-Generation Cosmic Microwave Background Polarimetry Experiment

We report on the development of commercially fabricated multi-chronic antenna-coupled transition edge sensor (TES) bolometer arrays for cosmic microwave background (CMB) polarimetry experiments. The orders of magnitude increase in detector count for next-generation CMB experiments require a new approach in detector wafer production to increase fabrication throughput. Here, we describe collaborative efforts with a commercial superconductor electronics fabrication facility (SeeQC, Inc.) to fabricate antenna-coupled TES bolometer detectors. We have successfully fabricated an operational dual-polarization, dichroic sinuous antenna-coupled TES detector array on a 150-mm-diameter wafer. The fabricated detector arrays have average yield of 95% and excellent uniformity across the wafer. Both RF characteristics and TES bolometer properties are suitable for CMB observations. We successfully fabricated different types of TES bolometers optimized for frequency multiplexing readout, time-domain multiplexing readout, and microwave SQUID multiplexing readout. We also demonstrated high production throughput. We discuss the motivation, design considerations, fabrication processes, test results, and how industrial detector fabrication could be a path to fabricate hundreds of detector wafers for future CMB polarimetry experiments.

47 OTHER INSTRUMENTATION↗