Results for “neuromorphic” — Page 30

NASA NTRSDate not supplied

Cloud-Based Demodulation and Data Distribution of a Satellite Downlink

Ground station networks connected to the cloud allow space missions to have global communications coverage without operating their own infrastructure. In this work, we describe the communications architecture for the TechEdSat-13 mission, which performed the first in-space characterization of a neuromorphic processor. The mission utilizes a commercial provider for S-band downlinks. A suite of cloud services and open-source software such as GNU Radio are leveraged to demodulate signals received by an AWS ground station during passes with TechEdSat-13 and store recovered data. Once a pass is scheduled, the entire process takes place without human intervention. On-orbit results the past year of operations are presented, demonstrating the advantages of this approach over traditional operator-owned ground stations. Use of software-defined radio makes possible custom signal processing. The homogeneity of apertures and their interfaces to the cloud simplifies scaling across many sites. This abundance of candidate links lays the groundwork for intelligent scheduling agents to optimize pass selection across several factors, automatically recover from failed contacts, and gather metrics to learn from past performance.

cloud demodulation↗

NASA NTRS2024

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗

NASA NTRSDate not supplied

SPIKE-Dx : A Low-Power High-Throughput Fault Diagnostics Tool using Spiking Neural Networks for Constrained Systems

Diagnostic systems are important for many aerospace systems, which are severely limited in available power, like cubesats or UAVs. Therefore, traditional diagnostics systems cannot be used due to their substantial footprint and constraints. In this paper, we present our very low power diagnostic tool SPIKE-DX to monitor critical systems with constrained computational and energy resources. This is made possible through spiking neural networks (SNNs), which are executable within optimized simulation environments and further implemented on on cutting-edge neuromorphic hardware. Based upon FMEA (Failure Mode and Effect Analysis) framework, Diagnostic Bayesian Networks (DBNs) can be constructed that provide powerful means for diagnostic reasoning. In this paper, we describe such DBNs and a method to automatically translate the DBN into highly structured networks of spiking neurons for execution in SPIKE-DX.

Spiking Neural Networks↗

NASA NTRSDate not supplied

NASA Small Spacecraft and Distributed Systems Program - Recent and Upcoming Technology Demonstrations and Development Efforts

NASA’s Small Spacecraft & Distributed Systems (SSDS) program strengthens U.S. ability to conduct unique missions by rapidly developing and demonstrating capabilities for SmallSat exploration, science, and commercial space. In collaboration with NASA Centers, other government agencies, commercial industry, and academia, SSDS advances next generation SmallSat technologies like power, processing, propulsion, communications, autonomous navigation, architectures (swarms), and applications (AI/ML/Edge Computing)—to extend missions beyond LEO into cislunar and planetary space. Various investment mechanisms exist for SSDS to select and fund projects that will ultimately advance NASA’s Moon to Mars Architecture. Presented here are the latest achievements and findings from recently completed SSDS projects, along with updates from ongoing efforts and planned future work. Successful missions like Starling and CAPSTONE continue to demonstrate their capability after several years on-orbit. Advancements in next generation swarm configurations are being implemented by Starling for space traffic monitoring and management applications. Findings from recent SSDS flight projects are discussed: DiskSat, a unique SmallSat platform alternative to canisterized nanosatellites, launched December 2025 and is gathering data; the PTD series of missions concluded in December 2025. Current SSDS efforts are focused on addressing NASA Shortfalls relating to rendezvous and proximity operations, neuromorphic computing, and space situational awareness.

Roger C Hunter↗

NASA NTRSDate not supplied

NASA Small Spacecraft and Distributed Systems: Recent and Upcoming Technology Demonstrations and Development Efforts

NASA’s Small Spacecraft & Distributed Systems (SSDS) program strengthens U.S. ability to conduct unique missions by rapidly developing and demonstrating capabilities for SmallSat exploration, science, and commercial space. In collaboration with NASA Centers, other government agencies, commercial industry, and academia, SSDS advances next generation SmallSat technologies like power, processing, propulsion, communications, autonomous navigation, architectures (swarms), and applications (AI/ML/Edge Computing)—to extend missions beyond LEO into cislunar and planetary space. Various investment mechanisms exist for SSDS to select and fund projects that will ultimately advance NASA’s Moon to Mars Architecture. Presented here are the latest achievements and findings from recently completed SSDS projects, along with updates from ongoing efforts and planned future work. Successful missions like Starling and CAPSTONE continue to demonstrate their capability after several years on-orbit. Advancements in next generation swarm configurations are being implemented by Starling for space traffic monitoring and management applications. Findings from recent SSDS flight projects are discussed: DiskSat, a unique SmallSat platform alternative to canisterized nanosatellites, launched December 2025 and is gathering data; the PTD series of missions concluded in December 2025. Current SSDS efforts are focused on addressing NASA Shortfalls relating to rendezvous and proximity operations, neuromorphic computing, and space situational awareness.

Roger Hunter↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

DOE OSTI2022

Carbon-based volatile and non-volatile memristors

An ultrathin, carbon-based memristor with a moiré superlattice potential shows prominent ferroelectric resistance switching. The memristor includes a bilayer material, such as Bernal-stacked bilayer graphene, encapsulated between two layers of a layered material, such as hexagonal boron nitride. At least one of the encapsulating layers is rotationally aligned with the bilayer to create the moiré superlattice potential. The memristor exhibits ultrafast and robust resistance switching between multiple resistance states at high temperatures. The memristor, which may be volatile or nonvolatile, may be suitable for neuromorphic computing.

Jarillo-Herrero, Pablo↗

DOE OSTI2021

High-Speed Ionic Synaptic Memory Based on 2D Titanium Carbide MXene

Synaptic devices with linear high-speed switching can accelerate learning in artificial neural networks (ANNs) embodied in hardware. Conventional resistive memories however suffer from high write noise and asymmetric conductance tuning, preventing parallel programming of ANN arrays. Electrochemical random-access memories (ECRAMs), where resistive switching occurs by ion insertion into a redox-active channel, aim to address these challenges due to their linear switching and low noise. ECRAMs using 2D materials and metal oxides however suffer from slow ion kinetics, whereas organic ECRAMs enable high-speed operation but face challenges toward on-chip integration due to poor temperature stability of polymers. Here, ECRAMs using 2D titanium carbide (Ti 3 C 2 T x ) MXene that combine the high speed of organics and the integration compatibility of inorganic materials in a single high-performance device are demonstrated. These ECRAMs combine the speed, linearity, write noise, switching energy, and endurance metrics essential for parallel acceleration of ANNs, and importantly, they are stable after heat treatment needed for back-end-of-line integration with Si electronics. The high speed and performance of these ECRAMs introduces MXenes, a large family of 2D carbides and nitrides with more than 30 stoichiometric compositions synthesized to date, as promising candidates for devices operating at the nexus of electrochemistry and electronics.

2D materials↗

DOE OSTI2025

Materials Selection Principles for Designing Electro‐Thermal Neurons

Artificial neurons exhibiting volatile threshold switching and action potential‐like oscillations are crucial for brain‐inspired computing. While Complimentary Metal‐Oxide‐Semiconductor (CMOS)‐based strategies require hundreds of transistors to simulate each neuron, neuronal oscillations arise spontaneously in individual electro‐thermal devices due to nonlinearities like the Mott transition in VO 2 . Despite improved understanding of the physics, quantitative connections between neuronal performance and material properties remain under‐explored, preventing predictive neuron design and rational materials selection. In this work, a physics‐aware forward design methodology is developed for interrogating a wide palette of materials with properties varying by orders of magnitude, and their performance (high frequency, high dynamical reconfigurability and low power) under external circuit and device geometry constraints is assessed. The space of viable materials is identified to be much larger than previously recognized, with candidates from a range of materials classes, including Ge, GaP and MoS 2 . CMOS‐compatible performance (such as 100 GHz oscillating frequencies) can be achieved with CMOS‐compatible node sizes (≈10 nm). Finally, combinations of material properties yielding desired neuronal performance under uncertain design constraints are considered. This work solidifies forward design principles for electro‐thermal neuron devices, a necessary pre‐condition for inverse design from desired neuronal performance to required materials properties.

compact model↗

DOE OSTI2022

All-Electric Nonassociative Learning in Nickel Oxide

Habituation and sensitization represent nonassociative learning mechanisms in both non-neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular-like learning in oxygen-deficient NiO x devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiO x under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next-generation neuroinspired computing hardware.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Charge Regulation Stabilizes the Formation of Ionic Liquid‐Based Amphiphilic Oligomer Droplet Interface Bilayers

Amphiphilic charged oligomers (oligodimethylsiloxane – methylimidazolium cation, ODMS-MIM (+) ), assemble into bilayers using the droplet interface bilayer (DIB) platform, possess similar size and functionality as phospholipid bilayers, but exhibit increased stability. The oligomer ionic headgroups (MIM (+) ) are covalently bound to monodisperse, short-chain (n = 13) hydrophobic tails (ODMS). These self-assemble as monolayer brushes at the oil–aqueous interface of water droplets that are influenced by both the charged cationic headgroups, and the nature of the covalently attached tails in the organic phase. Charge regulation (CR) stabilizes the formation of ordered, molecularly close-packed brush phases, which results in highly insulating, stable DIB membranes, with contributions from specific ion-pairing effects, Debye screening, and voltage-dependent electrocompressive stresses. In the oil phase, interactions between hexadecane, a good solvent for ODMS, and the hydrophobic tails result in extended waiting times for bilayer formation compared to phospholipid DIBs, for which hexadecane is a poor solvent. Close agreement between experimental values and predictions for two key parameters, the critical membrane thickness, h c , and maximal grafted headgroup density, Γ 0 , validate an electrostatic CR model consisting of adsorption and partial neutralization of counterions at a charged interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Protecting groups in insertion chemistry: Site-selective positioning of lithium ions in intercalation hosts

The expanding toolbox of functionalization chemistries for selectively installing moieties on specific sites of a substrate underpins almost all modern organic and biochemical syntheses. Likewise, the installation of mobile guest ions in specific interstitial sites and their transport along specific diffusion pathways forms the basis of modern lithium (Li)-ion battery electrodes. Many insertion hosts afford a diverse range of accessible interstitial sites. However, site-selective positioning of cations with atomic precision remains almost entirely unexplored. Here, we show that by deciphering site preferences of co/pre-intercalated cations, we can position Li ions in four distinct sites within a 1D ζ-V 2 O 5 insertion host. The use of topochemistry to effect single-crystal-to-single-crystal transformations enables atomic-resolution mapping of the selective positioning of Li ions through single-crystal X-ray diffraction. Here we image interstitial sites that are occupied at high depths of discharge, thereby obtaining unprecedented structural insight into “fully stuffed” frameworks critical for the realization of high-energy-density intercalation electrodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Efficient Probabilistic Computing with Stochastic Perovskite Nickelates

Probabilistic computing has emerged as a viable approach to solve hard optimization problems. Devices with inherent stochasticity can greatly simplify their implementation in electronic hardware. In this report we demonstrate intrinsic stochastic resistance switching controlled via electric fields in perovskite nickelates doped with hydrogen. The ability of hydrogen ions to reside in various metastable configurations in the lattice leads to a distribution of transport gaps. With experimentally characterized p-bits, a shared-synapse p-bit architecture demonstrates highly parallelized and energy-efficient solutions to optimization problems such as integer factorization and Boolean satisfiability. The results introduce perovskite nickelates as scalable potential candidates for probabilistic computing and showcase the potential of light-element dopants in next-generation correlated semiconductors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO 2 ) profoundly modifies the ground state properties. The electrical behavior of H x VO 2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. Here in this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a H x VO 2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO 2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information science

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Leveraging Low-Energy Structural Thermodynamics in Halide Perovskites

Not Available

14 SOLAR ENERGY↗

DOE OSTI2024

Axon-like active signal transmission

Any electrical signal propagating in a metallic conductor loses amplitude due to the natural resistance of the metal. Compensating for such losses presently requires repeatedly breaking the conductor and interposing amplifiers that consume and regenerate the signal. This century-old primitive severely constrains the design and performance of modern interconnect-dense chips. Here we present a fundamentally different primitive based on semi-stable edge of chaos (EOC), a long-theorized but experimentally elusive regime that underlies active (self-amplifying) transmission in biological axons. By electrically accessing the spin crossover in LaCoO 3 , we isolate semi-stable EOC, characterized by small-signal negative resistance and amplification of perturbations. In a metallic line atop a medium biased at EOC, a signal input at one end exits the other end amplified, without passing through a separate amplifying component. While superficially resembling superconductivity, active transmission offers controllably amplified time-varying small-signal propagation at normal temperature and pressure, but requires an electrically energized EOC medium. Operando thermal mapping reveals the mechanism of amplification—bias energy of the EOC medium, instead of fully dissipating as heat, is partly used to amplify signals in the metallic line, thereby enabling spatially continuous active transmission, which could transform the design and performance of complex electronic chips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Electrolytic gold plating, stripping, and ion transport dynamics through a solid-state iodide perovskite

The pronounced electrochemical reactivity between halide perovskites and metal electrodes can introduce mobile extrinsic metal ions which can cause device instability or enable novel functionalities. Here we systematically investigate the kinetics of gold cation (Au + ) migration in indium tin oxide (ITO)/methylammonium lead triiodide (MAPbI 3 )/Au model devices under long-term potentiostatic biasing. Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) analyses reveal that Au + ions, electrochemically generated at the Au anode, traverse the perovskite layer with diffusion coefficients on the order of 10 −11 to 10 −10 cm 2 s −1 and are subsequently reduced at the cathode as Au 0 clusters, resembling metal plating behavior in electrolytic cells and solid-state batteries during charging. Furthermore, reversing the applied bias strips the plated Au 0 , revealing reversibility suitable for bipolar resistive switching devices and providing direct evidence of the electrochemical and ionic nature of Au transport within the perovskite matrix. Quantitatively determining diffusion coefficients and ion concentrations provides foundational inputs for future drift-diffusion modelling opportunities and allows us to relate our findings to implications on long term operation of devices like photovoltaic modules. These results clearly demonstrate the solid-state electrochemical nature of perovskite devices, highlight methods to be more quantitative about ion transport properties, provide and emphasize the importance of disentangling electro-, photo-, photoelectrochemical processes for understanding device performance and unlocking new functionalities.

14 SOLAR ENERGY↗