arXiv subjects

Jean Anne C. Incorvia

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

35 records · Page 2Linked to original sources

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↗

arXiv2022

High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network

Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures. These computing systems can be further improved upon by using emerging technologies that are more efficient than CMOS for neural computation. Recent research has demonstrated memristors and spintronic devices in various neural network designs boost efficiency and speed. This paper presents a biologically inspired fully spintronic neuron used in a fully spintronic Hopfield RNN. The network is used to solve tasks, and the results are compared against those of current Hopfield neuromorphic architectures which use emerging technologies.

cs.NE↗

arXiv2022

Logical and Physical Reversibility of Conservative Skyrmion Logic

Magnetic skyrmions are nanoscale whirls of magnetism that can be propagated with electrical currents. The repulsion between skyrmions inspires their use for reversible computing based on the elastic billiard ball collisions proposed for conservative logic in 1982. Here we evaluate the logical and physical reversibility of this skyrmion logic paradigm, as well as the limitations that must be addressed before dissipation-free computation can be realized.

cs.ET↗

arXiv2022

All Electrical Control and Temperature Dependence of the Spin and Valley Hall Effect in Monolayer WSe2 Transistors

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 reflection spectra data is used to interpret the KR data and predict a lower-bound spin/valley lifetime of 4.1 ns below 90 K and 0.26 ns at 160 K. The excess spin and valley per unit length along the edge is calculated to be 109 per micron at 45 K, which corresponds to a spin/valley polarization on the edge of 6%. These results are important steps towards practical use of the SVHE.

cond-mat.mes-hall↗

arXiv2022

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

In neuromorphic computing, artificial synapses provide a multi-weight conductance state that is set based on inputs from neurons, analogous to the brain. Additional properties of the synapse beyond multiple weights can be needed, and can depend on the application, requiring the need for generating different synapse behaviors from the same materials. Here, we measure artificial synapses based on magnetic materials that use a magnetic tunnel junction and a magnetic domain wall. By fabricating lithographic notches in a domain wall track underneath a single magnetic tunnel junction, we achieve 4-5 stable resistance states that can be repeatably controlled electrically using spin orbit torque. We analyze the effect of geometry on the synapse behavior, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a straight 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 applied on streamed Fashion-MNIST data, we show that the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional neural network for CIFAR-100 image recognition, we show that the straight magnetic synapse achieves near-ideal inference accuracy, due to the stability of its resistance levels. This work shows multi-weight magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.

cond-mat.mes-hall↗

arXiv2021

Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence

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 from smaller amounts of data and using a more compact and energy-efficient network than the state-of-the-art non-adaptive ANNs used for cancer diagnosis. 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 from smaller datasets, compact architectures, energy-efficiency, fault-tolerance and can lead to the realization of general artificial intelligence.

physics.app-ph↗

arXiv2021

Skyrmion Logic Clocked via Voltage Controlled Magnetic Anisotropy

Magnetic skyrmions are exciting candidates for energy-efficient computing due to their non-volatility, detectability,and mobility. A recent proposal within the paradigm of reversible computing enables large-scale circuits composed ofdirectly-cascaded skyrmion logic gates, but it is limited by the manufacturing difficulty and energy costs associated withthe use of notches for skyrmion synchronization. To overcome these challenges, we therefore propose a skyrmion logicsynchronized via modulation of voltage-controlled magnetic anisotropy (VCMA). In addition to demonstrating theprinciple of VCMA synchronization through micromagnetic simulations, we also quantify the impacts of current den-sity, skyrmion velocity, and anisotropy barrier height on skyrmion motion. Further micromagnetic results demonstratethe feasibility of cascaded logic circuits in which VCMA synchronizers enable clocking and pipelining, illustrating afeasible pathway toward energy-efficient large-scale computing systems based on magnetic skyrmions.

cond-mat.mes-hall↗

arXiv2021

Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation

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 study, 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 work establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.

cond-mat.mes-hall↗

arXiv2020

Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions

Complementary metal oxide semiconductor (CMOS) devices display volatile characteristics, and are not well suited for analog applications such as neuromorphic computing. Spintronic devices, on the other hand, exhibit both non-volatile and analog features, which are well-suited to neuromorphic computing. Consequently, these novel devices are at the forefront of beyond-CMOS artificial intelligence applications. However, a large quantity of these artificial neuromorphic devices still require the use of CMOS, which decreases the efficiency of the system. To resolve this, we have previously proposed a number of artificial neurons and synapses that do not require CMOS for operation. Although these devices are a significant improvement over previous renditions, their ability to enable neural network learning and recognition is limited by their intrinsic activation functions. This work proposes modifications to these spintronic neurons that enable configuration of the activation functions through control of the shape of a magnetic domain wall track. Linear and sigmoidal activation functions are demonstrated in this work, which can be extended through a similar approach to enable a wide variety of activation functions.

cs.NE↗

arXiv2020

Domain Wall-Magnetic Tunnel Junction Spin Orbit Torque Devices and Circuits for In-Memory Computing

There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction (MTJ) memory elements can be used for computation by manipulating a domain wall (DW), a transition region between magnetic domains. But, these devices have suffered from challenges: spin transfer torque (STT) switching of a DW requires high current, and the multiple etch steps needed to create an MTJ pillar on top of a DW track has led to reduced tunnel magnetoresistance (TMR). These issues have limited experimental study of devices and circuits. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction (DW-MTJ) in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy (PMA), spin-orbit torque (SOT) switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 164%, resistance-area product RA = 31 Ω-μm^2, close to the RA of the unpatterned film, and lower switching current density compared to using spin transfer torque. A two-device circuit shows bit propagation between devices. Device initialization variation in switching voltage is shown to be curtailed to 7% by controlling the DW initial position, which we show corresponds to 96% accuracy in a DW-MTJ full adder simulation. These results make strides in using MTJs and DWs for in-memory and neuromorphic computing applications.

cond-mat.mes-hall↗

arXiv2020

Tunnel magnetoresistance in scandium nitride magnetic tunnel junctions using first principles

The magnetic tunnel junction is a cornerstone of spintronic devices and circuits, providing the main way to convert between magnetic and electrical information. In state-of-the-art magnetic tunnel junctions, magnesium oxide is used as the tunnel barrier between magnetic electrodes, providing 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 causes challenges of device-to-device variability and tunnel barrier degradation under high current. Here, we study using first principles narrower-bandgap scandium nitride tunneling properties and transport in magnetic tunnel junctions in comparison to magnesium oxide. These simulations demonstrate a high tunnel magnetoresistance in Fe/ScN/Fe MTJs via Δ_1 and Δ_2' symmetry filtering with low wavefunction decay rates, allowing a low resistance-area product. The results show that scandium nitride could be a new tunnel barrier material for magnetic tunnel junction devices to overcome variability and current-injection challenges.

cond-mat.mtrl-sci↗

arXiv2020

Hybrid Pass Transistor Logic with Ambipolar Transistors

In comparison to the conventional complementary pull-up and pull-down logic structure, the pass transistor logic (PTL) family reduces the number of transistors required to perform logic functions, thereby reducing both area and power consumption. However, this logic family requires inter-stage inverters to ensure signal integrity in cascaded logic circuits, and inverters must be used to provide each logical input signal in its complementary form. These inverters and complementary signals increase the device count and significantly degrade overall system efficiency. Dual-gate ambipolar field-effect transistors natively provide a single-transistor XNOR operation and permit highly-efficient and compact circuits due to their ambipolar capabilities. Similar to PTL, logic circuits based on ambipolar field-effect transistors require complementary signals. Therefore, numerous inverters are required, with significant energy and area costs. Ambipolar field-effect transistors are a natural match for PTL, as hybrid ambipolar-PTL circuits can simultaneously use these inverters to satisfy their necessity in both PTL and ambipolar circuits. We therefore propose a new hybrid ambipolar-PTL logic family that exploits the compact logic of PTL and the ambipolar capabilities of ambipolar field-effect transistors. Novel hybrid ambipolar-PTL circuits were designed and simulated in SPICE, demonstrating strong signal integrity along with the efficiency advantages of using the required inverters to simultaneously satisfy the requirements of PTL and ambipolar circuits. In comparison to the ambipolar field-effect transistors in the conventional CMOS logic structure, our hybrid full adder circuit can reduce propagation delay by 47%, energy consumption by 88%, energy-delay product by a factor of 9, and area-energy-delay product by a factor of 20.

cs.ET↗

arXiv2020

Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture

We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture. The proposed approach leverages the three-terminal structure of domain-wall magnetic tunnel junction devices to establish a feedback loop that serves to train such devices when they are used as synapses in a neuromorphic computing architecture.

cs.NE↗

arXiv2020

Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning

Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generalizes quickly (with few samples). We demonstrate interactions between physical properties of this device and optimal implementation of neuroscience-inspired plasticity learning rules, and highlight performance on a suite of tasks. Our energy analysis confirms the value of the approach, as the learning budget stays below 20 $μJ$ even for large tasks used typically in machine learning.

cs.NE↗

arXiv2020

CMOS-Free Multilayer Perceptron Enabled by Four-Terminal MTJ Device

Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information. To fully realize this potential, neuromorphic systems should exploit the biomimetic behavior of emerging nanodevices. In particular, exceptional opportunities are provided by the non-volatility and analog capabilities of spintronic devices. While spintronic devices have previously been proposed that emulate neurons and synapses, complementary metal-oxide-semiconductor (CMOS) devices are required to implement multilayer spintronic perceptron crossbars. This work therefore proposes a new spintronic neuron that enables purely spintronic multilayer perceptrons, eliminating the need for CMOS circuitry and simplifying fabrication.

cs.NE↗

arXiv2019

Exploiting Dual-Gate Ambipolar CNFETs for Scalable Machine Learning Classification

Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and reconfigurable computing. In this paper, AP-CNFETs are used to design a mixed-signal machine learning (ML) classifier. The classifier is designed in SPICE with feature size of 15 nm and operates at 250 MHz. The system is demonstrated based on MNIST digit dataset, yielding 90% accuracy and no accuracy degradation as compared with the classification of this dataset in Python. The system also exhibits lower power consumption and smaller physical size as compared with the state-of-the-art CMOS and memristor based mixed-signal classifiers.

cs.ET↗

arXiv2019

Shape-based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems. Previous proposals for spintronic artificial neurons required external stimuli to perform the leaking functionality, one of the three fundamental functions of a leaky integrate-and-fire (LIF) neuron. The use of this external magnetic field or electrical current stimulus results in either a decrease in energy efficiency or an increase in fabrication complexity. In this work, we modify the shape of previously demonstrated three-terminal magnetic tunnel junction neurons to perform the leaking operation without any external stimuli. The trapezoidal structure causes shape-based DW drift, thus intrinsically providing the leaking functionality with no hardware cost. This LIF neuron therefore promises to advance the development of spintronic neural network crossbar arrays.

physics.app-ph↗