Spin-orbit torque (SOT) induced by electric current has attracted extensive attention as an efficient method of controlling the magnetization in nanomagnetic structures. SOT-induced magnetization ...reversal is usually achieved with the aid of an in-plane bias magnetic field. In this paper, we show that by selecting a film stack with weak out-of-plane magnetic anisotropy, field-free SOT-induced switching can be achieved in micron sized multilayers. Using direct current, deterministic bipolar magnetization reversal is obtained in Pt/Co/Ni
/Co/Ta structures. Kerr imaging reveals that the SOT-induced magnetization switching process is completed via the nucleation of reverse domain and propagation of domain wall in the system.
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.
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.
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.
Understanding of domain wall (DW) propagation in a complex structure is an essential first step toward the development of any magnetic-domain based devices including spin-based logic or magnetic ...memristors. Interfacial Dzyaloshinskii-Moriya interaction (iDMI) in the structure with broken inversion symmetry induces an asymmetrical DW configuration with respect to the direction of in-plane field. Dynamic behaviors of field-driven DW within the film with perpendicular magnetic anisotropy is influenced by DW tilt from the iDMI effect and the corners in the T-shaped structure of the DW path. Images from Kerr microscopy reveal that the iDMI effective field contributes to a tilted structure of DW configuration and evolution along its propagation. With the combination of iDMI and T-shaped structure, we observed two distinguished bidirectional DW propagations in two output branches and distinct arriving times at the destination pads with a uniform external field. Micromagnetic simulation results is compared with the observed dynamics of a DW configuration in the structure providing an additional confirmation of the interpreted results.
Device Codesign using Reinforcement Learning Cardwell, Suma G.; Patel, Karan; Schuman, Catherine D. ...
2024 IEEE International Symposium on Circuits and Systems (ISCAS),
2024-May-19
Conference Proceeding
We demonstrate device codesign using reinforcement learning for probabilistic computing applications. We use a spin orbit torque magnetic tunnel junction model (SOT-MTJ) as the device exemplar. We ...leverage reinforcement learning (RL) to vary key device and material properties of the SOT-MTJ device for stochastic operation. Our RL method generated different candidate devices capable of generating stochastic samples for a given exponential distribution.
The hybrid semiconductor-ferromagnet structure has attracted much interest for spintronics applications which rely on spin injection/ tunneling from a ferromagnet into a semiconductor. The room ...temperature ferromagnetic metal MnAs is a key material in this dissertation work. MnAs was epitaxally grown on GaAs(001) and InAs(100) substrates by molecular beam epitaxy. The self-organized stripe form of MnAs/GaAs is discussed in the first part of the dissertation. The magnetic stripe pattern on MnAs is characterized using magnetic force microscopy (MFM) and magneto-optical Kerr effect (MOKE). With the saturation field applied to MnAs along its easy axis, a magneto-elastic stress on the periodic stripe pattern is created. The magneto-elastic strain causes a change of hysteresis measured with diffraction MOKE, not seen in either magnetization or typical MOKE measurement. The second part is a spintronic device study. Spin light emitting diodes are fabricated with MnAs/InAs heterostructures. This work focuses on measuring the injection of spins into the narrow gap semiconductor indium arsenide (InAs) from a MnAs spin-aligner and to study spin carrier transport and recombination mechanisms in an InAs quantum well. The experiment directly measures the optical polarization and compares the results to a rate equation simulation. The analysis shows that the spin-LEDs gave a maximum optical polarization of 17% at 7K. The final part of the dissertation deals with a new ferromagnetic metal MnGa/GaAs. Most electro-optical spintronic devices favor a spin polarizer aligned to the out-of-plane direction of the sample plane (perpendicular to the film plane). This is related to the quantization geometry for the states in the quantum well. The magnetization easy axis of MnGa is out-of-plane. The properties of MnGa are characterized with topography, composition, magnetization and metallicity measurements. The formation of Mn2As at the MnGa/GaAs interface is observed by X-ray diffraction (XRD). Topographic domains are measured by atomic force microscopy (AFM) and scanning electron microscopy (SEM). The ferromagnetic and electronic properties of the MnGa films are characterized using vibrating sample magnetometer (VSM) and scanning tunneling microscopy/ spectroscopy (STM/ STS), respectively.
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.
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