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DOE OSTI2021

Sulfurization Engineering of One–Step Low–Temperature MoS 2 and WS 2 Thin Films for Memristor Device Applications

2D materials have been of considerable interest as new materials for device applications. Non-volatile resistive switching applications of MoS 2 and WS 2 have been previously demonstrated; however, these applications are dramatically limited by high temperatures and extended times needed for the large-area synthesis of 2D materials on crystalline substrates. The experimental results demonstrate a one-step sulfurization method to synthesize MoS 2 and WS 2 at 550 °C in 15 min on sapphire wafers. Furthermore, a large area transfer of the synthesized thin films to SiO 2 /Si substrates is achieved. Following this, MoS 2 and WS 2 memristors are fabricated that exhibit stable non-volatile switching and a satisfactory large on/off current ratio (10 3 –10 5 ) with good uniformity. Tuning the sulfurization parameters (temperature and metal precursor thickness) is found to be a straightforward and effective strategy to improve the performance of the memristors. Furthermore, the demonstration of large-scale MoS 2 and WS 2 memristors with a one-step low-temperature sulfurization method with simple strategy to tuning can lead to potential applications such as flexible memory and neuromorphic computing.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Photoinduced Melting of V 4 O 7 Correlated State

The compound V 4 O 7 is one of the Magnéli phase (V n O 2n - 1 , n = 3, 4, …, 9) correlated vanadium oxides with distinct intriguing electronic and structural properties. The possibility to manipulate the phase state of V 4 O 7 on an ultrafast time scale by light makes this material promising for potential applications in photonics, optoelectronics, quantum, and neuromorphic circuit design. In this work, the ultrafast spectroscopy of V 4 O 7 reveals the second-order nature of the photoinduced insulator-to-metal transition, emphasizing electronic and lattice contributions. The findings reveal the influence of the laser excitation level and temperature on these dynamics, providing a comprehensive understanding of V 4 O 7 structural changes and response to external stimuli. The phenomenological model based on the Landau–Ginzburg formalism provides a robust framework for explaining the photoinduced transition dynamics, showing a detailed picture of the light interaction with the electronic and lattice subsystems. This integrated approach significantly enhances the understanding of V 4 O 7 complex behavior upon photoexcitation, opening new possibilities for developing new optoelectronic devices and noninvasive optical control of the phase transition pathways in vanadates.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Time- and strain-dependent nanoscale structural degradation in phase change epitaxial strontium ferrite films

Topotactic phase transition between metallic, perovskite SrFeO 3 and insulating, Brownmillerite SrFeO 2.5 has been extensively studied due to the potential applications in resistive switching devices for neuromorphic computing. However, its practical utilization as memristors has been hindered by the structural instability of SrFeO 3 , which is often ascribed to the generation of oxygen vacancies to form SrFeO 3-δ . Here we reveal the dominating defects generated in SrFeO 3 epitaxial thin films are atomic scale gaps as a result of interfacial strain. Our correlated time- and strain-dependent measurements show that tensile strained SrFeO 3 films form vertical, nanoscale gaps that are SrO-rich, which are accountable for the observed metal-to-insulator transition over time. While compressively strained or small lattice mismatched SrFeO 3 films mainly yield horizontal gaps with a smaller impact on the in-plane transport. The atomic scale origin of such defects and their impact on device performance need to be further understood in order to integrate phase change materials in oxide electronics.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Observation of single-defect memristor in an MoS2 atomic sheet

Non-volatile resistive switching, also known as memristor effect, where an electric field switches the resistance states of a two-terminal device, has emerged as an important concept in the development of high-density information storage, computing and reconfigurable systems. The past decade has witnessed substantial advances in non-volatile resistive switching materials such as metal oxides and solid electrolytes. It was long believed that leakage currents would prevent the observation of this phenomenon for nanometre-thin insulating layers. However, the recent discovery of non-volatile resistive switching in two-dimensional monolayers of transition metal dichalcogenide and hexagonal boron nitride sandwich structures (also known as atomristors) has refuted this belief and added a new materials dimension owing to the benefits of size scaling. Here we elucidate the origin of the switching mechanism in atomic sheets using monolayer MoS 2 as a model system. Atomistic imaging and spectroscopy reveal that metal substitution into a sulfur vacancy results in a non-volatile change in the resistance, which is corroborated by computational studies of defect structures and electronic states. These findings provide an atomistic understanding of non-volatile switching and open a new direction in precision defect engineering, down to a single defect, towards achieving the smallest memristor for applications in ultra-dense memory, neuromorphic computing and radio-frequency communication systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Synthetic antiferromagnet-based spin Josephson oscillator

Two easy-plane ferromagnetic layers, antiferromagnetically coupled through a thin nonmagnetic metal layer, form the magnetic analog of a Josephson junction. Additionally, a current driven spin chemical potential drives a 2π precession of the in-plane magnetization of each ferromagnet. The participation of the full magnetic moment in the 2π precession maximizes the giant magnetoresistance and the ac output power. The frequency can be continuously tuned by a dc bias. An applied ac bias results in a time-averaged magnetoresistance with Shapiro-like steps. The multistate mode-locking behavior exhibited by the Shapiro steps may be exploited for applications such as microwave detectors and neuromorphic computing. They may also serve as an experimental signature of spin superfluidity.

42 ENGINEERING↗

DOE OSTI2023

Effect of substrate and growth method on vanadium dioxide thin films by RF magnetron sputtering: Vanadium metal oxidation vs reactive sputtering

Vanadium dioxide (VO 2 ) undergoes a metal–insulator phase transition at ~70 °C and has attracted substantial interest for potential applications in electronics, including those in neuromorphic computing. The vanadium–oxygen system has a rather complicated phase diagram, and controlling the stoichiometry and the phase of thin films of vanadium oxides is a well-known challenge. Here, we explore the novel combination of two methods of VO 2 thin film deposition using off-axis RF magnetron sputtering on (100)- and (111)-oriented yttria-stabilized zirconia (YSZ) substrates: reactive sputtering of vanadium in an oxygen environment and sputtering of vanadium metal followed by oxidation to VO 2 . Interestingly, the reactive sputtering process on both substrate orientations yields the metastable semiconducting VO 2 (B) phase, which is structurally stabilized by the YSZ surface. The metal sputtering and oxidation process on YSZ produces mainly the equilibrium monoclinic (or M1) phase of VO 2 that exhibits a metal–insulator transition. Using this method, we obtained thin films of (010)-textured polycrystalline VO 2 (M1) that show a metal–insulator transition with an on/off ratio larger than 1000.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Comparing Exciton-Polariton Couplings in Two-Dimensional MoS2 Cavities

Exciton-polaritons (EPs) are an emerging approach to achieve strong light-matter coupling and robust quantum phenomena, with application to areas such as photonic neuromorphic computing for instance reservoir computing. 2D transition metal dichalcogenides (TMDs) are promising candidates to host EPs due to their potential for strong coupling as evidenced by large Rabi splitting energies, which in the case of few-layer MoS2 has been demonstrated to reach 293 meV when coupled to the C exciton, placing it in the ultrastrong coupling regime, at room temperature. Here we examine how the Rabi splitting of CVD-grown monolayer MoS2 in a cavity can be optimized by tuning two variables: cavity geometry (thickness of silver and dielectric spacers) and the targeted exciton for coupling (A at about 1.9 eV, B at about 2.06 eV, or C at about 2.9 eV). With the cavity geometry, there is a tradeoff between optical field confinement and transmission. With the targeted exciton, there may be a tradeoff between Rabi splitting and lifetime. Transfer matrix simulations are used to inform how thick the cavity layers need to be, and at what angle measurements should be performed, to achieve Rabi splitting at a certain exciton energy. Experimentally, the Rabi splitting is measured by placing the cavity at a specified angle and measuring the optical reflectance and transmittance. By fitting the data, we find Rabi splittings of 124 meV, 194 meV, and 288 meV for the A, B, and C excitons, respectively, which places them in the strong, and on the edge of the ultrastrong, coupling regime.

exciton-polaritons↗

DOE OSTI2021

Neuromorphic Graph Algorithms

Graph algorithms enable myriad large-scale applications including cybersecurity, social network analysis, resource allocation, and routing. The scalability of current graph algorithm implementations on conventional computing architectures are hampered by the demise of Moore’s law. We present a theoretical framework for designing and assessing the performance of graph algorithms executing in networks of spiking artificial neurons. Although spiking neural networks (SNNs) are capable of general-purpose computation, few algorithmic results with rigorous asymptotic performance analysis are known. SNNs are exceptionally well-motivated practically, as neuromorphic computing systems with 100 million spiking neurons are available, and systems with a billion neurons are anticipated in the next few years. Beyond massive parallelism and scalability, neuromorphic computing systems offer energy consumption orders of magnitude lower than conventional high-performance computing systems. We employ our framework to design and analyze new spiking algorithms for shortest path and dynamic programming problems. Our neuromorphic algorithms are message-passing algorithms relying critically on data movement for computation. For fair and rigorous comparison with conventional algorithms and architectures, which is challenging but paramount, we develop new models of data-movement in conventional computing architectures. This allows us to prove polynomial-factor advantages, even when we assume a SNN consisting of a simple grid-like network of neurons. To the best of our knowledge, this is one of the first examples of a rigorous asymptotic computational advantage for neuromorphic computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Neuromorphic Photonics Based on Phase Change Materials

Neuromorphic photonics devices based on phase change materials (PCMs) and silicon photonics technology have emerged as promising solutions for addressing the limitations of traditional spiking neural networks in terms of scalability, response delay, and energy consumption. In this review, we provide a comprehensive analysis of various PCMs used in neuromorphic devices, comparing their optical properties and discussing their applications. We explore materials such as GST (Ge 2 Sb 2 Te 5 ), GeTe-Sb 2 Te 3 , GSST (Ge 2 Sb 2 Se 4 Te 1 ), Sb 2 S 3 /Sb 2 Se 3 , Sc 0.2 Sb 2 Te 3 (SST), and In 2 Se 3 , highlighting their advantages and challenges in terms of erasure power consumption, response rate, material lifetime, and on-chip insertion loss. By investigating the integration of different PCMs with silicon-based optoelectronics, this review aims to identify potential breakthroughs in computational performance and scalability of photonic spiking neural networks. Further research and development are essential to optimize these materials and overcome their limitations, paving the way for more efficient and high-performance photonic neuromorphic devices in artificial intelligence and high-performance computing applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

In Situ Study of Resistive Switching in a Nitride‐Based Memristive Device

Abstract Resistive switching (RS) devices with ultra‐low‐voltage threshold and reliable switching repeatability exhibits great potential applications in energy‐efficient data storage and neuromorphic computing. Understanding switching mechanisms at nanoscale is critical to design RS devices with improved performance. In this work, a lamella memristive device using focused ion beam (FIB) method based on the metal/TiO x /TiN/Si structure device is fabricated. In situ transmission electron microscopy (TEM) and current–voltage ( I–V ) characteristic demonstrate that the lamella device shows a volatile RS behavior with a threshold switching at ≈ ± 0.4 V. In situ scanning transmission electron microscopy (STEM) experiments with electron energy loss spectroscopy (EELS) reveal that the charge carriers such as oxygen vacancies migrate under positive/negative DC bias and modulate Schottky barriers at the top and bottom metal/semiconductor interfaces. The RS mechanism of the lamella device is based on the Schottky barriers modulation and Joule heating assisted electric field triggered thermal runaway (FTTR) occurred at the metal/semiconductor interfaces. The fundamental insights gained from this study presents a perspective on interface‐type RS devices processing and opens up new technological opportunities of fabricating ultra‐low‐energy memristive devices.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fabrication and characterization of boron-terminated tetravacancies in monolayer hBN using STEM, EELS and electron ptychography

Tetravacancies in monolayer hexagonal boron nitride (hBN) with consistent edge termination (boron or nitrogen) form triangular nanopores with electrostatic potentials that can be leveraged for applications such as selective ion transport and neuromorphic computing. In order to quantitatively predict the properties of these structures, an atomic-level understanding of their local electronic and chemical environments is required. Moreover, robust methods for their precision manufacture are needed. Here we use electron irradiation in a scanning transmission electron microscope (STEM) at a high dose rate to drive the formation of boron-terminated tetravacancies in monolayer hBN. Characterization of the defects is achieved using aberration-corrected STEM, monochromated electron energy-loss spectroscopy (EELS), and electron ptychography. Z-contrast in STEM and chemical fingerprinting by core-loss EELS enable identification of the edge terminations, while electron ptychography gives insight into structural relaxation of the tetravacancies and provides evidence of enhanced electron density around the defect perimeters indicative of bonding effects.

2D hBN↗

DOE OSTI2023

Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor

Neuromorphic processors have garnered considerable interest in recent years for their potential in enabling energy-efficient and high-speed computing. The Locally Competative Algorithm (LCA) has been utilized for power efficient sparse coding on neuromophic processors, including the first Loihi processor \cite{appletospikes, loihi1}. With the Loihi 2 processor enabling custom neuron models and graded spike communication, more complex implementations of LCA are possible \cite{loihi2}. We present a new implementation of LCA designed for the Loihi 2 processor and perform an initial set of benchmarks comparing it to LCA on CPU and GPU devices. In these experiments LCA on Loihi 2 is faster and orders of magnitude more efficient, while maintaining similar reconstruction quality. We find this performance improvement increases as the LCA parameters are tuned towards greater representation sparsity. Our study highlights the potential of neuromorphic processors, particularly Loihi 2, in enabling intelligent,autonomous, real-time processing on small robots, satellite where there are strict SWaP (small, lightweighr, and low-power) requirement. By demonstrating the superior performance of LCA on Loihi 2 compared to conventional computing device, our study suggests that Loihi 2 could be a valuable tool in advancing these types of applications. Overall, our study highlights the potential of neuromorphic processors for efficient and accurate data processing on resource-constrained devices.

Parpart, Gavin G.↗

DOE OSTI2022

Shape‐Dependent Multi‐Weight Magnetic Artificial Synapses for Neuromorphic Computing

Abstract In neuromorphic computing, artificial synapses provide a multi‐weight (MW) conductance state that is set based on inputs from neurons, analogous to the brain. Herein, artificial synapses based on magnetic materials that use a magnetic tunnel junction (MTJ) and a magnetic domain wall (DW) are explored. By fabricating lithographic notches in a DW track underneath a single MTJ, 3–5 stable resistance states that can be repeatably controlled electrically using spin‐orbit torque are achieved. The effect of geometry on the synapse behavior is explored, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a rectangular device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application‐specific synaptic functions. Implementing an artificial neural network (NN) applied to streamed Fashion‐MNIST data, the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional NN for CIFAR‐100 image recognition, the rectangular magnetic synapse achieves near‐ideal inference accuracy, due to the stability of its resistance levels. This work shows MW magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.

Leonard, Thomas↗

DOE OSTI2020

Caspian: A Neuromorphic Development Platform

Current neuromorphic systems often may be difficult to use and costly to deploy. There exists a need for a simple yet flexible neuromorphic development platform which can allow researchers to quickly prototype ideas and applications. Caspian offers a high-level API along with a fast spiking simulator to enable the rapid development of neuromorphic solutions. It further offers an FPGA architecture that allows for simplified deployment -- particularly in SWaP (size, weight, and power) constrained environments. Leveraging both software and hardware, Caspian aims to accelerate development and deployment while enabling new researchers to quickly become productive with a spiking neural network system.

Mitchell, Parker↗

NASA NTRS2023

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.

Artificial Intelligence↗

DOE OSTI2025

Purely electronic insulator-metal transition in rutile VO 2

Volatile resistive switching in neuromorphic computing can be tuned by external stimuli such as temperature or electric-field. However, this type of switching is generally coupled to structural changes, resulting in slower reaction speed and higher energy consumption when incorporated into an electronic device. The vanadium dioxide (VO 2 ), which has near room temperature metal-insulator transition (MIT), is an archetypical volatile resistive switching system. Here, we demonstrate an isostructural MIT in an ultrathin VO 2 film capped with a photoconductive cadmium sulfide (CdS) layer. Transmission electron microscopy, resistivity experiments, and first-principles calculations show that the hole carriers induced by CdS photovoltaic effect are driving the MIT in rutile VO 2 . The insulating-rutile VO 2 phase has been proved and can remain stable for hours. Our finding provides a new approach to produce purely electronically driven MIT in VO 2 , and widens its applications in fast-response, low-energy neuromorphic devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Leveraging dendritic complexity for neuromorphic computing

Abstract Beyond-von Neumann computing approaches are necessary to sustain the growth of microelectronics and the increasing appetite for artificial intelligence/machine learning algorithms. Neuromorphic computing is an emerging paradigm that takes inspiration from the brain to provide a path forward to improve the computational efficiency and computational density of next-generation computing architectures. In nature, we observe brains performing complex computations with a much smaller energy footprint than conventional computing approaches. Current neuromorphic systems are focused primarily on scalability, namely, increasing the number of computational units (neurons) and connections between units (synapses). However, for brain-like cognition and efficiency in next-generation computing hardware, we need increased complexity in function, as well as improved connection density for scalability. Here, we present our work that aims to incorporate dendrites for ‘compute-on-wire’ in neuromorphic architectures to increase the computational complexity (e.g. number of programmable parameters, nonlinear dynamics) as well as computational efficiency (energy/compute) of artificial neural networks (ANNs). We do this by showcasing neuromorphic dendrite elements that can be leveraged for various applications. We will present examples of neuroscience-inspired direction-selective circuits and an ANN with active dendrites leveraging shunting inhibition. We also demonstrate the benefits of using dendrites in deep neural networks. To conclude, we discuss how we can utilize emerging hardware devices in these systems and design next-generation neuromorphic architectures with dendrites.

Cardwell, Suma G. (ORCID:0000000226575545)↗

DOE OSTI2022

AI-enhanced Codesign for Next-Generation Neuromorphic Circuits and Systems

This report details work that was completed to address the Fiscal Year 2022 Advanced Science and Technology (AS&T) Laboratory Directed Research and Development (LDRD) call for “AI-enhanced Co-Design of Next Generation Microelectronics.” This project required concurrent contributions from the fields of 1) materials science, 2) devices and circuits, 3) physics of computing, and 4) algorithms and system architectures. During this project, we developed AI-enhanced circuit design methods that relied on reinforcement learning and evolutionary algorithms. The AI-enhanced design methods were tested on neuromorphic circuit design problems that have real-world applications related to Sandia’s mission needs. The developed methods enable the design of circuits, including circuits that are built from emerging devices, and they were also extended to enable novel device discovery. We expect that these AI-enhanced design methods will accelerate progress towards developing next-generation, high-performance neuromorphic computing systems.

42 ENGINEERING↗