Microelectronics Research Center: https://www.mrc.utexas.edu/

Texas Materials Institute: https://tmi.utexas.edu/

Center for Dynamics and Control of Materials: https://mrsec.utexas.edu/

Sandia RadEdge team

DOE COINFLIPS Co-Design Team: https://coinflipscomputing.org/

Southwest Research Institute Energize Program: https://energy.utexas.edu/research/energize-program

UT Austin Portugal Program: https://utaustinportugal.org/

1. National Science Foundation

2. Sandia National Laboratories

3. US Department of Energy

4. Samsung Electronics

5. Intel

6. UT Portugal Program

7. Southwest Research Institute (SWRI)

Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. Device candidates for this application need to produce highly random ‘‘coinflips’’ while also having tunable biasing of the coin. The magnetic tunnel junction (MTJ) has been studied as an RNG due to its thermally-driven magnetization dynamics, often using spin transfer torque (STT) current amplitude to control the random switching of the MTJ free layer (FL) magnetization, here called the stochastic write method. There are additional knobs to control the MTJ- RNG, including voltage-controlled magnetic anisotropy (VCMA) and spin orbit torque (SOT), and there is a need to systematically study and compare these methods. We build an analytical model of the MTJ to characterize using VCMA and SOT to generate random bit streams. The results show that both methods produce high-quality, uniformly distributed bitstreams. Biasing the bitstreams using either STT current or an applied magnetic field shows a sigmoidal distribution versus bias amplitude for both VCMA and SOT, compared to less sigmoidal for stochastic write. The energy consumption per sample is calculated to be 0.1 pJ (SOT), 1 pJ (stochastic write), and 20 pJ (VCMA), revealing the potential energy benefit of using SOT and showing using VCMA may require higher damping materials. The generated bitstreams are then applied to two tasks: generating an arbitrary probability distribution and using the MTJ-RNGs as stochastic neurons to perform simulated annealing, where both VCMA and SOT methods show the ability to effectively minimize the system energy with a small delay and low energy. These results show the flexibility of the MTJ as a true RNG and elucidate design parameters for optimizing the device operation for applications.

The safety of nano-enabled devices must be assessed throughout the device lifecycle. Landfills are the end-of-life repository for functional devices; however, the dynamism of leachate age and complexity of leachate chemistry make material safety assessments challenging. This study evaluates the transformation and effects of water treatment membranes and transistors enabled with MoS2—a transition metal dichalcogenide (TMD) used in electronic transistors and futuristic wearable electronics—in complex landfill conditions. Device decay and MoS2 transformation are studied with a suite of characterization tools. The effects of MoS2 colloids on microbial diversity in young and mature landfill leachates are investigated. Results indicate that MoS2-enabled membranes and transistors become attached to residues from the leachate but do not undergo major chemical decay. Furthermore, complex environments like leachates are robust, resisting microbiome changes upon exposure to MoS2. The results obtained under the specific study conditions indicate that disposal of MoS2-enabled devices in landfills does not induce significant deleterious effects on the landfill in the absence of photo-transformation (as is the case in covered landfills). However, further studies are needed to assess whether a high concentration of MoS2, a likely result of its accumulation in the landfill over time, has the potential to substantially change the leachate microbiome.

The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain’s ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co-design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware-based probabilistic computing technologies.

Bayesian neural networks (BNNs) combine the generalizability of deep neural networks (DNNs) with a rigorous quantification of predictive uncertainty, which mitigates overfitting and makes them valuable for high-reliability or safety-critical applications. However, the probabilistic nature of BNNs makes them more computationally intensive on digital hardware and so far, less directly amenable to acceleration by analog in-memory computing as compared to DNNs. This work exploits a novel spintronic bit cell that efficiently and compactly implements Gaussian-distributed BNN values. Specifically, the bit cell combines a tunable stochastic magnetic tunnel junction (MTJ) encoding the trained standard deviation and a multi-bit domain-wall MTJ device independently encoding the trained mean. The two devices can be integrated within the same array, enabling highly efficient, fully analog, probabilistic matrix-vector multiplications. We use micromagnetics simulations as the basis of a system-level model of the spintronic BNN accelerator, demonstrating that our design yields accurate, well-calibrated uncertainty estimates for both classification and regression problems and matches software BNN performance. This result paves the way to spintronic in-memory computing systems implementing trusted neural networks at a modest energy budget.

Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain’s intellectual abilities. An impressive ability of the brain is its adaptive intelligence, which allows the brain to regulate its functions “on the fly” to cope with myriad and ever-changing situations. In particular, the brain displays three adaptive and advanced intelligence abilities of context-awareness, cross frequency coupling, and feature binding. To mimic these adaptive cognitive abilities, we design and simulate a novel, hardware-based adaptive oscillatory neuron using a lattice of magnetic skyrmions. Charge current fed to the neuron reconfigures the skyrmion lattice, thereby modulating the neuron’s state, its dynamics and its transfer function “on the fly.” This adaptive neuron is used to demonstrate the three cognitive abilities, of which context-awareness and cross-frequency coupling have not been previously realized in hardware neurons. Additionally, the neuron is used to construct an adaptive artificial neural network (ANN) and perform context-aware diagnosis of breast cancer. Simulations show that the adaptive ANN diagnoses cancer with higher accuracy while learning faster and using a more compact and energy-efficient network than a nonadaptive ANN. The work further describes how hardware-based adaptive neurons can mitigate several critical challenges facing contemporary ANNs. Modern ANNs require large amounts of training data, energy, and chip area, and are highly task-specific; conversely, hardware-based ANNs built with adaptive neurons show faster learning, compact architectures, energy-efficiency, fault-tolerance, and can lead to the realization of broader artificial intelligence.

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.

Heavy metal-based two-dimensional van der Waals materials have a large, coupled spin and valley Hall effect (SVHE) that has potential use in spintronics and valleytronics. Optical measurements of the SVHE have largely been performed below 30 K, and understanding of the SVHE-induced spin/valley polarizations that can be electrically generated is limited. Here, we study the SVHE in monolayer p-type tungsten diselenide (WSe2). Kerr rotation (KR) measurements show the spatial distribution of the SVHE at different temperatures, its persistence up to 160 K, and that it can be electrically modulated via gate and drain bias. A spin/valley drift and diffusion model together with a reflection measurement and a four-port electrical measurement is used to interpret the KR data. A lower-bound spin/valley lifetime is predicted to be of 40 ns and a mean free path of 240 nm below 90 K, 2 orders of magnitude higher than a previous work that uses similar methods. The spin/valley polarization on the edge is calculated to be ∼4% at 45 K. These results are important steps toward practical use of the SVHE.

CMOS-based computing systems that employ the von Neumann architecture
are relatively limited when it comes to parallel data storage and processing. In
contrast, the human brain is a living computational signal processing unit that
operates with extreme parallelism and energy efficiency. Although numerous
neuromorphic electronic devices have emerged in the last decade, most of
them are rigid or contain materials that are toxic to biological systems. In this
work, we report on biocompatible bilayer graphene-based artificial synaptic
transistors (BLAST) capable of mimicking synaptic behavior. The BLAST

devices leverage a dry ion-selective membrane, enabling long-term potentia-
tion, with ~50 aJ/μm2 switching energy efficiency, at least an order of magni-
tude lower than previous reports on two-dimensional material-based artificial

synapses. The devices show unique metaplasticity, a useful feature for gen-
eralizable deep neural networks, and we demonstrate that metaplastic BLASTs

outperform ideal linear synapses in classic image classification tasks. With
switching energy well below the 1 fJ energy estimated per biological synapse,
the proposed devices are powerful candidates for bio-interfaced online
learning, bridging the gap between artificial and biological neural networks.

The state-of-the-art magnetic tunnel junction, a cornerstone of spintronic devices and circuits, uses a magnesium oxide tunnel barrier that provides a uniquely large tunnel magnetoresistance at room temperature. However, the wide bandgap and band alignment of magnesium oxide-iron systems increases the resistance-area product and creates variability and breakdown challenges. Here, the authors study using first principles narrower-bandgap scandium nitride (ScN) transport properties in magnetoresistive junctions in comparison to magnesium oxide. The results show a high magnetoresistance in Fe/ScN/Fe via Δ₁and Δ2′ symmetry filtering with low wave function decay rates, suggesting scandium nitride could be a new barrier material for spintronic devices.

MXenes are a large class of materials that are chemically exfoliated from metal–aluminum–carbon (MAX) bulk crystals into low-dimensional sheets. While many MXenes have been theoretically predicted, the careful balance required in the exfoliation between breaking the inter-layer bonds without damaging the intra-layer bonds of the sheets has limited synthesis and experimental study. Here, we developed the synthesis of Cr2C from its parent Cr2AlC MAX phase and showed the etching is optimized using sodium fuoride and hydrogen chloride with a modifed minimally intensive layer delamination (mMILD) method in a cold environment of 9 °C. We further optimized the intercalation and delamination using sonication and washing methods. The resulting Cr2C crystal structure was characterized. These results open up Cr2C to experimental study, including of its predicted emergent magnetic properties, and develop guidelines for synthesizing new MXene materials.

We study the impact of irradiation on magnetic tunnel junction (MTJ) films with perpendicular magnetic anisotropy (PMA) and spin-orbit torque (SOT) switching using magneto-optical Kerr effect and transmission electron microscopy. Our results show that the thin-film stack is robust to gamma ionizing dose up to 1 Mrad(Si) and Ta ¹⁺ ion irradiation fluences up to 10 ¹² ions/cm ² , showing SOT PMA MTJs are radiation-hard. But, at very high Ta ¹⁺ ion irradiation between 10 ¹² and 10 ¹⁴ ions/cm ² , reduced coercivity and eventually greatly reduced PMA are observed, corresponding with an increase in intermixing of the CoFeB-MgO layers, particularly at the lower CoFeB-MgO interface. These results agree with displacement damage modeling that predicts higher damage in the layers closer to the bottom heavy metal and substrate. Compared to spin transfer torque and in-plane anisotropy MTJs, needing a top-pinned stack, a thicker heavy metal layer, and perpendicular anisotropy that is pinned out of plane by interfaces, all could make SOT PMA MTJs more susceptible to damage at high doses.

Inspired by the parallelism and efficiency of the brain, several candidates for artificial synapse devices have been developed for neuromorphic computing, yet a nonlinear and asymmetric synaptic response curve precludes their use for backpropagation, the foundation of modern supervised learning. Spintronic devices—which benefit from high endurance, low power consumption, low latency, and CMOS compatibility—are a promising technology for memory, and domain-wall magnetic tunnel junction (DW-MTJ) devices have been shown to implement synaptic functions such as long-term potentiation and spike-timing dependent plasticity. In this work, we propose a notched DW-MTJ synapse as a candidate for supervised learning. Using micromagnetic simulations at room temperature, we show that notched synapses ensure the non-volatility of the synaptic weight and allow for highly linear, symmetric, and reproducible weight updates using either spin transfer torque (STT) or spin–orbit torque (SOT) mechanisms of DW propagation. We use lookup tables constructed from micromagnetics simulations to model the training of neural networks built with DW-MTJ synapses on both the MNIST and Fashion-MNIST image classification tasks. Accounting for thermal noise and realistic process variations, the DW-MTJ devices achieve classification accuracy close to ideal floating-point updates using both STT and SOT devices at room temperature and at 400 K. Our work establishes the basis for a magnetic artificial synapse that can eventually lead to hardware neural networks with fully spintronic matrix operations implementing machine learning.

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this letter, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This letter establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

There have been recent efforts towards the development of biologically-inspired neuromorphic devices and architecture. Here, we show a synapse circuit that is designed to perform spike-timing-dependent plasticity which works with the leaky, integrate, and fire neuron in a neuromorphic computing architecture. The circuit consists of a three-terminal magnetic tunnel junction with a mobile domain wall between two low-pass filters and has been modeled in SPICE. The results show that the current flowing through the synapse is highly correlated to the timing delay between the pre-synaptic and post-synaptic neurons. Using micromagnetic simulations, we show that introducing notches along the length of the domain wall track pins the domain wall at each successive notch to properly respond to the timing between the input and output current pulses of the circuit, producing a multi-state resistance representing synaptic weights. We show in SPICE that a notch-free ideal magnetic device also shows spike-timing dependent plasticity in response to the circuit current. This work is key progress towards making more bio-realistic artificial synapses with multiple weights, which can be trained online with a promise of CMOS compatibility and energy efficiency.

There have been recent efforts towards the development of biologically-inspired neuromorphic devices and architecture. Here, we show a synapse circuit that is designed to perform spike-timing-dependent plasticity which works with the leaky, integrate, and fire neuron in a neuromorphic computing architecture. The circuit consists of a three-terminal magnetic tunnel junction with a mobile domain wall between two low-pass filters and has been modeled in SPICE. The results show that the current flowing through the synapse is highly correlated to the timing delay between the pre-synaptic and post-synaptic neurons. Using micromagnetic simulations, we show that introducing notches along the length of the domain wall track pins the domain wall at each successive notch to properly respond to the timing between the input and output current pulses of the circuit, producing a multi-state resistance representing synaptic weights. We show in SPICE that a notch-free ideal magnetic device also shows spike-timing dependent plasticity in response to the circuit current. This work is key progress towards making more bio-realistic artificial synapses with multiple weights, which can be trained online with a promise of CMOS compatibility and energy efficiency.