arXiv subjects

Jean Anne C. Incorvia

Publications and source records attributed to Jean Anne C. Incorvia.

At least 19 recordsLinked to original sources

arXiv2026

Octupole-driven spin torque switching of antiferromagnetic tunnel junctions

Magnetic tunnel junctions (MTJs) based on ferromagnets are canonical devices in spintronics, with wide-ranging applications in data storage, computing, and sensing. They simultaneously exhibit mechanisms for electrical detection and control of magnetic order through the tunneling magnetoresistance (TMR) and spin-transfer torque (STT) effects, respectively. It was long assumed that neither of these effects could be sizeable in all-antiferromagnetic tunnel junctions (AATJs), since they exhibit no net magnetization. Recently, however, it was shown that AATJs based on chiral antiferromagnets do exhibit TMR due to their non-relativistic momentum-dependent spin polarization and cluster magnetic octupole moment (CMO), which are manifestations of their spin-split band structure. However, the reciprocal effect, i.e., the antiferromagnetic counterpart of STT, has been assumed non-existent due to the total electric current being spin-neutral. Here, we report nanoscale AATJs exhibiting this reciprocal effect, which we term octupole-driven spin-transfer torque (OTT). We demonstrate current-induced OTT switching of PtMn3|MgO|PtMn3 AATJs, exhibiting a TMR value of 363% at room temperature and switching current densities of the order of 10 MA/cm2. Our theoretical modeling explains the origin of OTT in terms of the imbalance between intra- and inter-sublattice spin currents across the AATJ, and equivalently, in terms of the non-zero net cluster octupole polarization of each PtMn3 layer. This work establishes a new materials platform for antiferromagnetic spintronics and provides a pathway towards deeply scaled magnetic memory and room-temperature terahertz technologies.

cond-mat.mtrl-sci↗

arXiv2025

Evaluation of Wafer-Scale SOT-MRAM for Analog Crossbar Array Applications

Analog crossbar arrays consisting of emerging memory devices can greatly alleviate the computational strain required by vector matrix multiplications for neural network applications. The ability to produce spin orbit torque-magnetic random-access memory (SOT-MRAM) at wafer-scale positions SOT-MRAM as a strong memory candidate. In this work, we fabricate and measure 300 mm-compatible SOT-MRAM with 150% tunnel magnetoresistance ratio, fast (2 ns) and low voltage (<1 V) operation, low energy dissipation (350 fJ), low write noise (0.1%), and low device-to-device variation of 10%. Through 2-bit quantization aware training and noisy training as mitigation techniques, the measured SOT-MRAM devices attain 95% on MNIST. The bi-stable anisotropy and stochastic switching of SOT-MRAM can additionally be leveraged for stochastic training of binary neural networks, able to reach ideal accuracy for a single device. Lastly, the devices were evaluated on implementation of probabilistic graph modeling and the interplay of tunnel magnetoresistance ratio, probability curve distribution, and conductance noise was shown to reduce potential errors in implementation. Through these results, SOT-MRAM is shown to be a uniquely effective candidate for implementation of crossbar accelerators in memory- and energy-limited applications, able to take advantage of stochastic operation and bi-stability to beneficial results in neural network applications.

cond-mat.mes-hall↗

arXiv2025

Semiconductor quantum well magnetic memory using confinement from proximity exchange fields for high magnetoresistances in a field-effect transistor

There is a growing demand for highly-performant memories and memristive technologies for use in in-memory computing. Magnetic tunnel junctions (MTJs) have thus far addressed this need in the field of spintronics. Despite their low write power and high speeds, MTJs are limited by their modest on/off ratio at room temperature, which motivates a search for beyond-MTJ spintronic devices. In this work, we propose a device that uses two layers of ferromagnetic insulator (FMI) cladding a semiconductor QW, which is able to modulate the QW bandgap via electronic confinement resulting from proximity magnetization at the interfaces of the quantum well depending on the relative magnetization of the FMI layers. We predict that this device has the potential for very high magnetoresistances (MRs) possibly exceeding 10,000% at room temperature. We also predict that this device will operate with maximal MR in charge neutrality, and that electrostatic gating may promote the device to act as a magnetic memtransistor. This motivates the search for candidate materials and ultimately experimental demonstration of magnetic QW memories or memtransistors, which may have the potential to advance the state of the art in logic, memory, or neuromorphic circuits.

cond-mat.mes-hall↗

arXiv2024

AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators

Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consuming and tedious. Here, we investigate the design and optimization of spin orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation. We leverage reinforcement learning and evolutionary optimization to vary key device and material properties of the various device models for stochastic operation. Our AI guided codesign methods generated different candidate devices capable of generating stochastic samples for a desired probability distribution, while also minimizing energy usage for the devices.

cs.ET↗

arXiv2024

Skin Controlled Electronic and Neuromorphic Tattoos

Wearable human activity sensors developed in the past decade show a distinct trend of becoming thinner and more imperceptible while retaining their electrical qualities, with graphene e-tattoos, as the ultimate example. A persistent challenge in modern wearables, however, is signal degradation due to the distance between the sensor's recording site and the signal transmission medium. To address this, we propose here to directly utilize human skin as a signal transmission medium as well as using low-cost gel electrodes for rapid probing of 2D transistor-based wearables. We demonstrate that the hypodermis layer of the skin can effectively serve as an electrolyte, enabling electrical potential application to semiconducting films made from graphene and other 2D materials placed on top of the skin. Graphene transistor tattoos, when biased through the body, exhibit high charge carrier mobility (up to 6500 2V-1s-1), with MoS2 and PtSe2 transistors showing mobilities up to 30 cm2V-1s-1 and 1 cm2V-1s-1, respectively. Finally, by introducing a layer of Nafion to the device structure, we observed neuromorphic functionality, transforming these e-tattoos into neuromorphic bioelectronic devices controlled through the skin itself. The neuromorphic bioelectronic tattoos have the potential for developing self-aware and stand-alone smart wearables, crucial for understanding and improving overall human performance.

cs.HC↗

arXiv2024

Impact of external magnetic fields on STT-MRAM

This application note discusses the working principle of spin-transfer torque magnetoresistive random access memory (STT-MRAM) and the impact that magnetic fields can have on STT-MRAM operation. Sources of magnetic field and typical magnitudes of magnetic fields are given. Based on the magnitude of commonly encountered external magnetic fields, we show below that magnetic immunity of STT-MRAM is sufficient for most uses once the chip is mounted on a printed circuit board (PCB) or inserted in its working environment. This statement is supported by the experience acquired during 60 years of use of magnetic hard disk drives (HDD) including 20 years of HDD with readers comprising magnetic tunnel junctions, 20+ years of use of magnetic field sensors as position encoders in automotive industry and 15+ years of use of MRAM. Mainly during chip handling does caution need to be exercised to avoid exposing the chip to excessively high magnetic fields.

physics.app-ph↗

arXiv2024

Kinematic Model of Magnetic Domain Wall Motion for Fast, High-Accuracy Simulations

Domain wall (DW) devices have garnered recent interest for diverse applications including memory, logic, and neuromorphic primitives; fast, accurate device models are therefore imperative for large-scale system design and verification. Extant DW motion models are sub-optimal for large-scale system design either over-consuming compute resources with physics-heavy equations or oversimplifying the physics, drastically reducing model accuracy. We propose a DW model inspired by the phenomenological similarities between motions of a DW and a classical object being acted on by forces like air resistance or static friction. Our proposed phenomenological model predicts DW motion within 1.2% on average compared with micromagnetic simulations that are 400 times slower. Additionally our model is seven times faster than extant collective coordinate models and 14 times more accurate than extant hyper-reduced models making it an essential tool for large-scale DW circuit design and simulation. The model is publicly posted along with scripts that automatically extract model parameters from user-provided simulation or experimental data to extend the model to alternative micromagnetic parameters.

cs.ET↗

arXiv2024

Domain Wall Magnetic Tunnel Junction Reliable Integrate and Fire Neuron

In spiking neural networks, neuron dynamics are described by the biologically realistic integrate-and-fire model that captures membrane potential accumulation and above-threshold firing behaviors. Among the hardware implementations of integrate-and-fire neuron devices, one important feature, reset, has been largely ignored. Here, we present the design and fabrication of a magnetic domain wall and magnetic tunnel junction based artificial integrate-and-fire neuron device that achieves reliable reset at the end of the integrate-fire cycle. We demonstrate the domain propagation in the domain wall racetrack (integration), reading using a magnetic tunnel junction (fire), and reset as the domain is ejected from the racetrack, showing the artificial neuron can be operated continuously over 100 integrate-fire-reset cycles. Both pulse amplitude and pulse number encoding is demonstrated. The device data is applied on an image classification task using a spiking neural network and shown to have comparable performance to an ideal leaky, integrate-and-fire neural network. These results achieve the first demonstration of reliable integrate-fire-reset in domain wall-magnetic tunnel junction-based neuron devices and shows the promise of spintronics for neuromorphic computing.

cs.NE↗

arXiv2024

Tunable Spike-Timing-Dependent Plasticity in Magnetic Skyrmion Manipulation Chambers

Magnetic skyrmions, as scalable and non-volatile spin textures, can dynamically interact with fields and currents, making them promising for unconventional computing. This paper presents a neuromorphic device based on skyrmion manipulation chambers to implement spike-timing-dependent plasticity (STDP), a mechanism for unsupervised learning in brain-inspired computing. STDP adjusts synaptic weights based on the timing of pre- and post-synaptic spikes. The proposed three-chamber design encodes synaptic weight in the number of skyrmions in the center chamber, with left and right chambers for pre-synaptic and post-synaptic spikes. Micromagnetic simulations demonstrate that the timing between applied currents across the chambers controls the final skyrmion count (weight). The device exhibits adaptability and learning capabilities by manipulating chamber parameters, mimicking Hebbian and dendritic location-based plasticity. The device's ability to maintain state post-write highlights its potential for advancing adaptable neuromorphic devices.

cond-mat.mes-hall↗

arXiv2024

Roadmap for Unconventional Computing with Nanotechnology

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

cs.ET↗

arXiv2023

Low voltage local strain enhanced switching of magnetic tunnel junctions

Strain-controlled modulation of the magnetic switching behavior in magnetic tunnel junctions (MTJs) could provide the energy efficiency needed to accelerate the use of MTJs in memory, logic, and neuromorphic computing, as well as an additional way to tune MTJ properties for these applications. State-of-the-art CoFeB-MgO based MTJs still require too high voltages to alter their magnetic switching behavior with strain. In this study, we demonstrate strain-enhanced field switching of nanoscale MTJs through electric field control via voltage applied across local gates. The results show that record-low voltage down to 200 mV can be used to control the switching field of the MTJ through enhancing the magnetic anisotropy, and that tunnel magnetoresistance is linearly enhanced with voltage through straining the crystal structure of the tunnel barrier. These findings underscore the potential of electric field manipulation and strain engineering as effective strategies for tailoring the properties and functionality of nanoscale MTJs.

cond-mat.mtrl-sci↗

arXiv2023

Magnetic Tunnel Junction Random Number Generators Applied to Dynamically Tuned Probability Trees Driven by Spin Orbit Torque

Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNG) can consume orders of magnitude less energy per bit than CMOS pseudo-RNG. Here, we numerically investigate with a macrospin Landau-Lifshitz-Gilbert equation solver the use of pMTJs driven by spin-orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called 'coinflips', via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval 0,255 from an exponential distribution based on p-value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a 'bucket' to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, iwth diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNG, showing faster bit generation and significantly lower energy use.

cond-mat.mes-hall↗

arXiv2023

Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems

Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and gaussian dendritic potentials. The devices can be variably connected to enable higher order neuronal responses, and we show through data-driven spiking neural network classification simulations that overall spiking activity is reduced by up to 15% without accuracy loss while low frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.

cond-mat.mes-hall↗

arXiv2023

Cascaded Logic Gates Based on High-Performance Ambipolar Dual-Gate WSe2 Thin Film Transistors

Ambipolar dual-gate transistors based on two-dimensional (2D) materials, such as graphene, carbon nanotubes, black phosphorus, and certain transition metal dichalcogenides (TMDs), enable reconfigurable logic circuits with suppressed off-state current. These circuits achieve the same logical output as CMOS with fewer transistors and offer greater flexibility in design. The primary challenge lies in the cascadability and power consumption of these logic gates with static CMOS-like connections. In this article, high-performance ambipolar dual-gate transistors based on tungsten diselenide (WSe2) are fabricated. A high on-off ratio of 10^8 and 10^6, a low off-state current of 100 to 300 fA, a negligible hysteresis, and an ideal subthreshold swing of 62 and 63 mV/dec are measured in the p- and n-type transport, respectively. For the first time, we demonstrate cascadable and cascaded logic gates using ambipolar TMD transistors with minimal static power consumption, including inverters, XOR, NAND, NOR, and buffers made by cascaded inverters. A thorough study of both the control gate and polarity gate behavior is conducted, which has previously been lacking. The noise margin of the logic gates is measured and analyzed. The large noise margin enables the implementation of VT-drop circuits, a type of logic with reduced transistor number and simplified circuit design. Finally, the speed performance of the VT-drop and other circuits built by dual-gate devices are qualitatively analyzed. This work lays the foundation for future developments in the field of ambipolar dual-gate TMD transistors, showing their potential for low-power, high-speed and more flexible logic circuits.

cond-mat.mtrl-sci↗

arXiv2023

Parallel Matrix Multiplication Using Voltage Controlled Magnetic Anisotropy Domain Wall Logic

The domain wall-magnetic tunnel junction (DW-MTJ) is a versatile device that can simultaneously store data and perform computations. These three-terminal devices are promising for digital logic due to their nonvolatility, low-energy operation, and radiation hardness. Here, we augment the DW-MTJ logic gate with voltage controlled magnetic anisotropy (VCMA) to improve the reliability of logical concatenation in the presence of realistic process variations. VCMA creates potential wells that allow for reliable and repeatable localization of domain walls. The DW-MTJ logic gate supports different fanouts, allowing for multiple inputs and outputs for a single device without affecting area. We simulate a systolic array of DW-MTJ Multiply-Accumulate (MAC) units with 4-bit and 8-bit precision, which uses the nonvolatility of DW-MTJ logic gates to enable fine-grained pipelining and high parallelism. The DW-MTJ systolic array provides comparable throughput and efficiency to state-of-the-art CMOS systolic arrays while being radiation-hard. These results improve the feasibility of using domain wall-based processors, especially for extreme-environment applications such as space.

cond-mat.mes-hall↗

arXiv2023

Stochastic Domain Wall-Magnetic Tunnel Junction Artificial Neurons for Noise-Resilient Spiking Neural Networks

The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring advantages of scalability and energy efficiency. Domain wall (DW) based magnetic tunnel junction (MTJ) devices are well suited for probabilistic neural networks given their intrinsic integrate-and-fire behavior with tunable stochasticity. Here, we present a scaled DW-MTJ neuron with voltage-dependent firing probability. The measured behavior was used to simulate a SNN that attains accuracy during learning compared to an equivalent, but more complicated, multi-weight (MW) DW-MTJ device. The validation accuracy during training was also shown to be comparable to an ideal leaky integrate and fire (LIF) device. However, during inference, the binary DW-MTJ neuron outperformed the other devices after gaussian noise was introduced to the Fashion-MNIST classification task. This work shows that DW-MTJ devices can be used to construct noise-resilient networks suitable for neuromorphic computing on the edge.

cs.NE↗

arXiv2023

Domain Wall-Magnetic Tunnel Junction Analog Content Addressable Memory Using Current and Projected Data

With the rise in in-memory computing architectures to reduce the compute-memory bottleneck, a new bottleneck is present between analog and digital conversion. Analog content-addressable memories (ACAM) are being recently studied for in-memory computing to efficiently convert between analog and digital signals. Magnetic memory elements such as magnetic tunnel junctions (MTJs) could be useful for ACAM due to their low read/write energy and high endurance, but MTJs are usually restricted to digital values. The spin orbit torque-driven domain wall-magnetic tunnel junction (DW-MTJ) has been recently shown to have multi-bit function. Here, an ACAM circuit is studied that uses two domain wall-magnetic tunnel junctions (DW-MTJs) as the analog storage elements. Prototype DW-MTJ data is input into the magnetic ACAM (MACAM) circuit simulation, showing ternary CAM function. Device-circuit co-design is carried out, showing that 8-10 weight bits are achievable, and that designing asymmetrical spacing of the available DW positions in the device leads to evenly spaced ACAM search bounds. Analyzing available spin orbit torque materials shows platinum provides the largest MACAM search bound while still allowing spin orbit torque domain wall motion, and that the circuit is optimized with minimized MTJ resistance, minimized spin orbit torque material resistance, and maximized tunnel magnetoresistance. These results show the feasibility of using DW-MTJs for MACAM and provide design parameters.

cond-mat.mes-hall↗

arXiv2022

Random Bitstream Generation using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions

Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. 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 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 need to systematically study and compared 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 an approximately sigmoidal distribution vs. bias amplitude for both VCMA and SOT, compared to less sigmoidal for stochastic write for the same conditions. 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 example tasks: generating an arbitrary probability distribution using the Hidden Correlation Bernoulli Coins method and using the MTJ-RNGs as stochastic neurons to perform simulated annealing in a Boltzmann machine, where both VCMA and SOT methods show the ability to effectively minimize the system energy with 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.

cond-mat.mes-hall↗