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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

In-pixel AI for lossy data compression at source for X-ray detectors

Integrating neural networks for data compression directly in the Read-Out Integrated Circuits (ROICs), i.e. the pixelated front-end, would result in a significant reduction in off-chip data transfer, overcoming the I/O bottleneck. Our ROIC test chip (AI-In-Pixel-65) is designed in a 65 nm Low Power CMOS process for the readout of pixelated X-ray detectors. Each pixel consists of an analog front-end for signal processing and a 10b analog-to-digital converter operating at 100KSPS. Here, we compare two non-reconfigurable techniques, Principal Component Analysis (PCA) and an AutoEncoder (AE) as lossy data compression engines implemented within the pixelated area. The PCA algorithm achieves 50$×$ compression, adds one clock cycle latency, and results in a 21% increase in the pixel area. The AE achieves 70$×$ compression, adds 30 clock cycle latency, and results in a similar area increase.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Digital fabrication of a small diameter polymer optical waveguide

A novel polymer optical waveguide and method of manufacturing is presented herein. A digitally manufactured process is described which utilizes a micro-dispensed UV optical adhesive as the contour guiding cladding, a fused deposition modeling technology for creating a core, and a subtractive laser process to finish the two ends of the optical interconnect. The optical waveguide can be printed directly on a circuit board in some embodiments. Alternatively, using a slightly modified process including a step to bond the optical fiber to the substrate, the optical interconnect can be manufactured on a flexible substrate.

Bhethanabotla, Venkat Rama↗
DOE OSTI2024

Scattering wave packets of hadrons in gauge theories: Preparation on a quantum computer

Quantum simulation holds promise of enabling a complete description of high-energy scattering processes rooted in gauge theories of the Standard Model. A first step in such simulations is preparation of interacting hadronic wave packets. To create the wave packets, one typically resorts to adiabatic evolution to bridge between wave packets in the free theory and those in the interacting theory, rendering the simulation resource intensive. In this work, we construct a wave-packet creation operator directly in the interacting theory to circumvent adiabatic evolution, taking advantage of resource-efficient schemes for ground-state preparation, such as variational quantum eigensolvers. By means of an ansatz for bound mesonic excitations in confining gauge theories, which is subsequently optimized using classical or quantum methods, we show that interacting mesonic wave packets can be created efficiently and accurately using digital quantum algorithms that we develop. Specifically, we obtain high-fidelity mesonic wave packets in the Z 2 and U(1) lattice gauge theories coupled to fermionic matter in 1+1 dimensions. Our method is applicable to both perturbative and non-perturbative regimes of couplings. The wave-packet creation circuit for the case of the Z 2 lattice gauge theory is built and implemented on the Quantinuum H1-1 trapped-ion quantum computer using 13 qubits and up to 308 entangling gates. The fidelities agree well with classical benchmark calculations after employing a simple symmetry-based noise-mitigation technique. This work serves as a step toward quantum computing scattering processes in quantum chromodynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrated Spatial, Spectral, & Temporal Optical Reflectance System for Precision Occupancy & Location Sensing to Improve Building Energy Efficiency

Buildings consume approximately 35% of the electricity used in the U.S. and building owners can significantly reduce this energy use by providing services like heating, electrical power and lighting only when people are present. The ARPAe funded program titled “INTEGRATED SPATIAL, SPECTRAL, & TEMPORAL OPTICAL REFLECTANCE SYSTEM FOR PRECISION OCCUPANCY & LOCATION SENSING TO IMPROVE BUILDING ENERGY EFFICIENCY” demonstrates how a low cost sensor technology developed for measuring distances can be used to count and locate occupants with a high degree of precision with a very low error rates. This platform tells a building control system where occupants are located (but not who they are) so that energy consuming services can be provided only when the services are needed by building occupants. The original proof of concept involved using low cost, commercially available time-of-flight (TOF) sensors that measure distance, but the performance of these existing sensors was lacking, as they could not operate properly in the presence of sunlight, which blinded the simple TOF sensors and limited their utility in buildings. This project proposed a powerful new class of TOF sensors that used state-of-the-art integrated circuit (IC) fabrication processes that combined advanced photonics with conventional silicon chip circuitry for improved sensor performance. An equally important part of this project was to find ways to maximize occupant count and location accuracy while using the fewest number of sensors possible, in order to keep costs low. By using building blueprints to create digital twins of commercial building spaces, the team developed new algorithms to maximize occupant count and tracking accuracy by properly locating the minimum number of sensors at just the right spots in the building. This capability not only minimizes system costs but also simplified sensor installation and system commissioning. Our simulations of our sensor networks for a range of commercial floorplan designs demonstrated that our installed cost target of $0.08/sqft was attainable, though not fully demonstrated during the project. Finally, we noted that the TOF sensor concept could provide a valuable role in health and eldercare by tracking patients without the need for worn sensors and would be useful for fall detection and other patient safety metrics, including tracking healthcare/patient interactions. We feel that, when fully developed, this new class of sophisticated TOF sensors and support software will be a powerful new approach to improving building energy efficiency based on occupant centric control platforms and will also open new levels of patient safety in healthcare operations. To realize this potential, the team formed the Troy Sensor Company LLC to oversee licensing of the programs patents and continue to seek commercialization of this program’s activity sensing technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Reliability Assessment of Solid-State Circuit Breakers (CRADA CRD-21-21469, Project 4 Final Report)

Growth in power requirements and the complexity of emerging distribution systems are creating the need for new solid-state products for many applications. The qualification requirements, testing standards and life-cycle management practices of solid-state devices used in protection applications is not established. The project will leverage newly developed accelerated testing capabilities design specifically for protection applications to generate large data sets that will enable advanced digital techniques for data analysis and embedded health monitoring.

14 SOLAR ENERGY↗
DOE OSTI2025

Semiconductor-compatible topological digital alloys

Recently, GeSn alloys have attracted much interest for direct-gap infrared photonics and as potential topological materials which are compatible with the semiconductor industry. However, for photonics, the high-Sn content required leads to low detectivity, associated with poor material quality, and the (>35 %) Sn required for topological properties have been out of reach experimentally. Here, we demonstrate that by patterning the Sn distribution within Ge, the electronic properties have a far greater tunability than is possible with the random alloy. For the GeSn δ-digital alloy (DA) formed by confining Sn atoms in atomic layer(s) along the [111] direction of Ge, we show that ∼ 10 % Sn can lead to a triple-point semimetal. These findings are understood in terms of Sn ordering causing spatial separation of Sn and Ge band edges, leading to band inversion. This mechanism can also lead to a weak topological insulator, Weyl semimetal, and enables tunable direct bandgaps down to 2 meV, covering the entire infrared range. This DA induced topological properties are also identified in compound semiconductors, such as InAs 1-x Sb x , showing the general applicability of the DA design for realizing topological properties on conventional semiconductor platforms. Furthermore, our findings not only point to a new class of currently unexplored topological systems accessible by epitaxy, but also establish the promise of low-Sn GeSn DAs for application as infrared laser diodes and photodetectors in Si photonic integrated circuits and infrared image sensors.

3D topological semimetal↗
DOE OSTI2025

A virtual Frisch-grid geometry-based CZT gamma detector for in-field radioisotope identification

Here, we present a Virtual Frisch-Grid geometry-based CZT gamma detector developed for identifying different radioisotopes over an energy range from a few keV up to 2 MeV, and useful for efficient characterization of CZT crystals. The detector is built with a 3 x 3 matrix of CZT crystals, each measuring approximately 6 mm x 6 mm x 15 mm. The charge generated within the sensor’s active volume is read out via an anode connected directly to the AVG3_Dev integrated circuit. A current signal induced by charge drift is collected on side pads of the crystals, enabling reconstruction of a 3D interaction position. This paper discusses the design, development, and performance of the standalone, mobile detector system, which integrates the AVG3_Dev readout IC developed at Brookhaven National Laboratory, a high-speed FPGA-based with per-channel digital signal processing, and embedded system capabilities. The device is compact, battery-powered, and supports wireless data streaming, making it suitable for field operations for radioisotope identification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Multi-channel front-end ASIC for a 3D position-sensitive detector

Arrays of 3D position-sensitive detectors (3DPSD), operating at room temperature and using cadmium zinc telluride (CZT) and thallium bromide (TIBr) sensors, are suitable for gamma-ray spectrometry in many applications. One detector configuration, the 3D position-sensitive Virtual Frisch-Grid detector (VFG), is particularly advantageous for integrating into large area arrays. The signals generated inside each detector of the array are captured with the anode, cathode and four pads that enable the reconstruction of the position and energy of the ionizing interaction by measurements of amplitude and timing of the signals. For these applications, a low-noise front-end ASIC has been developed, capable of processing bipolar signals (needed because of AC-coupling of certain electrodes). The ASIC can be coupled to an ADC in order to form a compound “waveform digitizer” capable of post-processing the analog signals and determining amplitude and timing information. This paper describes a 32-channel front-end ASIC that is suitable for reading out a 3 × 3 or 4 × 4 element matrix in the VFG configuration. Each channel is composed of a low-noise charge amplifier with an adaptive continuous reset feedback circuit suitable for both positive and negative charge, a first order shaper and a single-to-differential converter output stage. Voltage and current references are all internally generated by 10-bit DACs and the chip is fully controllable with the I 2 C communication protocol. The readout channel response has been verified using the implemented injection circuit. Linear behavior up to ~75 ke ± with the gain of ~80 mV/fC, and up to ~200 ke ± with the gain of ~30 mV/fC was demonstrated. In conclusion, the first test result waveforms using a 137 Cs radioactive source on a 5 × 5 × 12 mm 3 TIBr crystal are reported.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Realization of fermionic Laughlin state on a quantum processor

Strongly correlated topological phases of matter are central to modern condensed matter physics and quantum information technology but often challenging to probe and control in material systems. The experimental difficulty of accessing these phases has motivated the use of engineered quantum platforms for simulation and manipulation of exotic topological states. Among these, the Laughlin state stands as a cornerstone for topological matter, embodying fractionalization, anyonic excitations, and incompressibility. Although its bosonic analogs have been realized on programmable quantum simulators, a genuine fermionic Laughlin state has yet to be demonstrated on a quantum processor. Here, we realize the ν = 1/3 fermionic Laughlin state on IonQ’s trapped-ion quantum computer using an efficient and scalable Hamiltonian variational ansatz with 369 two-qubit gates on a 16-qubit circuit. Employing symmetry-verification error mitigation, we extract key observables that characterize the Laughlin state, including correlation hole, bulk-edge correspondence, and topological entanglement entropy, with strong agreement to exact diagonalization benchmarks. This work demonstrates an end-to-end workflow to simulate material-intrinsic topological orders and provides a starting point to explore its dynamics and excitations on digital quantum processors.

Shen, Lingnan [Univ. of Washington, Seattle, WA (U↗
DOE OSTI2022

Waveform processing using neural network algorithms on the front-end electronics

In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as “edge computing”. Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. Further, to better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Characterization of Digital Silicon Photomultipliers

Digital Silicon photomultipliers (Digital SiPMs) have become an increasingly viable option for single-photon detection due to their compact size and precise timing and resolution. The following work characterizes the noise performance of a diSiPM designed and tested in-house at Fermilab. The SiPM consists of a 26x32 array of single-photon avalanche diode (SPAD) cells. Through the implementation of a shift register masking circuit, individual SPAD cells can be enabled and disabled at will, allowing for precise detection measurements of single columns (accumulator channels) and cells. The provided circuit design operates on voltage clock pulses and collects data on clock edges allowing for precise tuning of the data collection process by varying the clock frequency, pulse width, and integration period.

Shoemaker, Oliver [U. Chicago (main)]↗
DOE OSTI2023

Generalized Quantum Convolution for Multidimensional Data

The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A 32-Channel Cryo-CMOS ASIC for SNSPD Biasing and Readout with Picosecond

Superconducting nanowire single-photon detectors (SNSPD) are a promising technology for particle detection. Although SNSPDs have demonstrated picosecond timing accuracy, scaling up large arrays has proved challenging. In this work, we introduce a 32-channel cryo-CMOS application-specific integrated circuit (ASIC) that can be tightly integrated with SNSPD arrays. The ASIC is designed to operate at a temperature of 4K and can perform up to 32 simultaneous timing measurements with a root-mean-square (RMS) accuracy of 8.0ps. The ASIC includes on-chip circuitry for externally biasing superconducting devices, low-noise amplifiers for reading superconducting devices, high-resolution time-to-digital converters (TDC) for time-tagging events, and serializers for transmitting data to room-temperature electronics. The ASIC is manufactured in a 22nm FDSOI process and occupies an area of 4.0mm x 1.0mm. The performance of the ASIC was verified using custom cryogenic device models internally developed for the 22nm SOI process. Measurement results will be presented at the conference.

Fredenburg, Jeff↗
DOE OSTI2024

A 32-Channel Cryo-CMOS ASIC for SNSPD Biasing and Readout with Picosecond Timing

Superconducting nanowire single-photon detectors (SNSPD) are a promising technology for particle detection. Although SNSPDs have demonstrated picosecond timing accuracy, scaling up large arrays has proved challenging. In this work, we introduce a 32-channel cryo-CMOS application-specifc integrated circuit (ASIC) that can be tightly integrated with SNSPD arrays. The ASIC is designed to operate at a temperature of 4K and can perform up to 32 simultaneous timing measurements with a root-mean-square (RMS) accuracy of 8.0ps. The ASIC includes on-chip circuitry for externally biasing superconducting devices, low-noise amplifers for reading superconducting devices, high-resolution time-to-digital converters (TDC) for time-tagging events, and serializers for transmitting data to room-temperature electronics. The ASIC is manufactured in a 22nm FDSOI process and occupies an area of 4.0mm x 1.0mm. The performance of the ASIC was verifed using custom cryogenic device models internally developed for the 22nm SOI process. Measurement results will be presented at the conference.

Fredenburg, Jeff↗
DOE OSTI2026

ML–Enabled FPGA Framework for Fast Quantum State Discrimination in Mid-Circuit Measurement Regimes

Accurate and low-latency quantum state discrimination is essential for protocols involving mid-circuit measurement (MCM) and conditional feed-forward. In superconducting quantum systems, conventional readout pipelines transfer measurement data to host processors for post-processing, introducing millisecond-scale delays that far exceed qubit coherence times. To overcome this bottleneck, we present an in-situ machine learning (ML) inference engine implemented on an FPGA for real-time quantum state discrimination. Our design performs inference directly on digitized readout signals with 40 ns latency, supports both qubit and qutrit readout, and enables conditional operations without host-side intervention. This capability is critical for MCM and for feedback-driven protocols such as quantum error correction. We validate the system on superconducting transmon hardware, demonstrating robust discrimination fidelity across multiple qubit and qutrit channels. We further demonstrate conditional qutrit logic driven by FPGA-resident classification, highlighting the potential of low-latency ML-on-FPGA control for NISQ applications and scalable fault-tolerant quantum computing.

Vora, Neel [Lawrence Berkeley National Laboratory ↗
DOE OSTI2021

Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features

The internetworking of grid-connected power electronics converters (PECs) in photovoltaic (PV) farms has inevitably expanded the cyber-attack surfaces. Here this paper presents a comprehensive study on cyber-attack detection and diagnosis for PEC-enabled PV farms via single waveform sensor to distinguish between normal conditions, open-circuit faults, short-circuit faults, and cyber-attacks. To our knowledge, this has not been attempted before. Firstly, we propose frequency-domain magnitude-based residuals to identify short-circuit faults and a time-domain mean current vector-based feature to distinguish open-circuit faults from other threats. These features can fully reflect the specific physical characteristics of PV farms during threat duration. Secondly, unlike micro phasor measurement units (µPMU) and raw electric waveform-based methods, the proposed innovative features can address novel cyber-attacks that are excluded from the training process. Thirdly, an online hardware-in-the-loop (HIL) testbed using the OPAL-RT real-time digital simulator has verified the effectiveness. The monitoring system runs in real-time while using HIL as an operational solar farm and a National Instruments (NI) data acquisition card as the electric waveform sensor at the point of coupling.

42 ENGINEERING↗
DOE OSTI2024

The SparkPix-S ASIC for the sparsified readout of 1 MHz frame-rate X-ray cameras at LCLS-II: pixel design and simulation results

Exploiting the “sparse” nature of the information in XPCS (X-ray Photon Correlation Spectroscopy) and XSVS (Speckle Visibility Spectroscopy) experiments, we present the SparkPix-S, a 3-sides buttable Application Specific Integrated Circuit (ASIC) based on a sparsified readout strategy for large-format hybrid detectors. The SparkPix-S architecture, based on the successful ePix family, will be composed as follows: a front-end 2-D matrix of 384×352 square pixels with 50 µm pitch is arranged to match the dimensions of a PIN Si-sensor matrix; charge readout, signal shaping and amplitude discrimination is performed at pixel-level, by means of a low-power (<18 µW) analog processor, which, in case of an event, negotiates access to an analog bus placed every other column; on the chip periphery (balcony), the information on each bus is digitized by an array of successive approximation analog-to-digital converters (SAR-ADCs) running at 10 Msps; on the digital back-end the global logic will generate the output data stream using low-voltage differential signalling (LVDS). A first prototype of the SparkPix-S, with a reduced matrix size of 96×96 pixels, is currently under production on a 130 nm CMOS technology. Simulated performance results show an equivalent noise charge <60 el. r.m.s. at 1 MHz repetition rate, with a maximum input energy of 60 keV and capability to discriminate charge signals with equivalent energy as low as 900 eV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Testing of Instrumentation and Control Sensors and Cables for Small Modular Reactors

As advanced reactor concepts like small modular reactors (SMRs) progress towards design maturity and commercial deployment, it is important to assess their instrumentation and control (I&C) system sensors and cables to assure their safe, reliable, and efficient operation throughout their service lifetimes. The I&C system of any nuclear power plant (NPP) is the central nervous system of the plant and is made up of field devices such as sensors which are connected via cables to analog and/or digital systems. These systems are responsible for control and/or protection of the plant. The performance of I&C sensors and cables is dependent on several factors, including plant operating conditions. In particular, the plant operation can affect the static (accuracy) and dynamic (response) performance of the sensor, whereas the surrounding ambient environment can impact the remaining useful life of the cables, connectors, and other associated components within I&C sensor circuits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗