Spin orbit torque (SOT) provides an efficient way to significantly reduce the current required for switching nanomagnets. However, SOT generated by an in-plane current cannot deterministically switch a perpendicularly polarized magnet due to symmetry reasons. On the other hand, perpendicularly polarized magnets are preferred over inplane magnets for high-density data storage applications due to their significantly larger thermal stability in ultrascaled dimensions. Here, we show that it is possible to switch a perpendicularly polarized magnet by SOT without needing an external magnetic field. This is accomplished by engineering an anisotropy in the magnets such that the magnetic easy axis slightly tilts away from the direction, normal to the film plane. Such a tilted anisotropy breaks the symmetry of the problem and makes it possible to switch the magnet deterministically. Using a simple Ta/CoFeB/MgO/Ta heterostructure, we demonstrate reversible switching of the magnetization by reversing the polarity of the applied current. This demonstration presents a previously unidentified approach for controlling nanomagnets with SOT.spin orbit torque | perpendicular anisotropy | nanomagnets S pin orbit coupling (SOC) and/or broken inversion symmetry in vertical heterostructures can generate accumulation of spins when a charge current is flowing through them. In doing so, it can exert a torque on an adjacent magnet (1-8). Indeed, high Z metals (Ta, Pt, W, etc.) with strong SOC have been used to inject spin currents into adjacent ferromagnetic layers and thereby to induce magnetic switching, oscillation, domain wall movement, etc. (1)(2)(3)(4)(5)(7)(8)(9). In a typical heterostructure such as Ta/CoFeB/MgO (from the bottom), an in-plane current flowing in the x direction (electrons flowing in the -x direction) generatesσ =ŷ polarized spins that accumulate at the Ta/CoFeB interface. Therefore, if the ferromagnet (CoFeB) is polarized in-plane, the spin accumulation can rotate it to the +y direction by a Slonczewski-like torqueτ sl = τ 0 sl

Spin-based computing schemes could enable new functionalities beyond those of charge-based approaches. Examples include nanomagnetic logic, where information can be processed using dipole coupled nanomagnets, as demonstrated by multi-bit computing gates. One fundamental benefit of using magnets is the possibility of a significant reduction in the energy per bit compared with conventional transistors. However, so far, practical implementations of nanomagnetic logic have been limited by the necessity to apply a magnetic field for clocking. Although the energy associated with magnetic switching itself could be very small, the energy necessary to generate the magnetic field renders the overall logic scheme uncompetitive when compared with complementary metal-oxide-semiconductor (CMOS) counterparts. Here, we demonstrate a nanomagnetic logic scheme at room temperature where the necessity for using a magnetic field clock can be completely removed by using spin-orbit torques. We construct a chain of three perpendicularly polarized CoFeB nanomagnets on top of a tantalum wire and show that an unpolarized current flowing through the wire can 'clock' the perpendicular magnetization to a metastable state. An input magnet can then drive the nanomagnetic chain deterministically to one of two dipole-coupled states, '2 up 1 down' or '2 down 1 up', depending on its own polarization. Thus, information can flow along the chain, dictated by the input magnet and clocked solely by a charge current in tantalum, without any magnetic field. A three to four order of magnitude reduction in energy dissipation is expected for our scheme when compared with state-of-the-art nanomagnetic logic.

We report a proof-of-concept demonstration of negative capacitance effect in a nanoscale ferroelectric-dielectric heterostructure. In a bilayer of ferroelectric Pb(Zr0.2Ti0.8)O3 and dielectric SrTiO3, the composite capacitance was observed to be larger than the constituent SrTiO3 capacitance, indicating an effective negative capacitance of the constituent Pb(Zr0.2Ti0.8)O3 layer. Temperature is shown to be an effective tuning parameter for the ferroelectric negative capacitance and the degree of capacitance enhancement in the heterostructure. Landau’s mean field theory based calculations show qualitative agreement with observed effects. This work underpins the possibility that by replacing gate oxides by ferroelectrics in nanoscale transistors, the sub threshold slope can be lowered below the classical limit (60 mV/decade).

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Spin-polarized electrons can move a ferromagnetic domain wall through the transfer of spin angular momentum when current flows in a magnetic nanowire. Such current induced control of a domain wall is of significant interest due to its potential application for low power ultra high-density data storage. In previous reports, it has been observed that the motion of the domain wall always happens parallel to the current flow – either in the same or opposite direction depending on the specific nature of the interaction. In contrast, here we demonstrate deterministic control of a ferromagnetic domain wall orthogonal to current flow by exploiting the spin orbit torque in a perpendicularly polarized Ta/CoFeB/MgO heterostructure in presence of an in-plane magnetic field. Reversing the polarity of either the current flow or the in-plane field is found to reverse the direction of the domain wall motion. Notably, such orthogonal motion with respect to current flow is not possible from traditional spin transfer torque driven domain wall propagation even in presence of an external magnetic field. Therefore the domain wall motion happens purely due to spin orbit torque. These results represent a completely new degree of freedom in current induced control of a ferromagnetic domain wall.

Resistive Random Access Memory (RRAM) and Phase Change Memory (PCM) devices have been popularly used as synapses in crossbar array based analog Neural Network (NN) circuit to achieve more energy and time efficient data classification compared to conventional computers. Here we demonstrate the advantages of recently proposed spin orbit torque driven Domain Wall (DW) device as synapse compared to the RRAM and PCM devices with respect to on-chip learning (training in hardware) in such NN. Synaptic characteristic of DW synapse, obtained by us from micromagnetic modeling, turns out to be much more linear and symmetric (between positive and negative update) than that of RRAM and PCM synapse. This makes design of peripheral analog circuits for on-chip learning much easier in DW synapse based NN compared to that for RRAM and PCM synapses. We next incorporate the DW synapse as a Verilog-A model in the crossbar array based NN circuit we design on SPICE circuit simulator. Successful on-chip learning is demonstrated through SPICE simulations on the popular Fisher's Iris dataset. Time and energy required for learning turn out to be orders of magnitude lower for DW synapse based NN circuit compared to that for RRAM and PCM synapse based NN circuits.

We propose a quantum classifier, which can classify data under the supervised learning scheme using a quantum feature space. The input feature vectors are encoded in a single quN it (a N level quantum system), as opposed to more commonly used entangled multi-qubit systems. For training we use the much used quantum variational algorithm-a hybrid quantum-classical algorithm, in which the forward part of the computation is performed on a quantum hardware whereas the feedback part is carried out on a classical computer. We introduce "single shot training" in our scheme, with all input samples belonging to the same class being used to train the classifier simultaneously. This significantly speeds up the training procedure and provides an advantage over classical machine learning classifiers. We demonstrate successful classification of popular benchmark datasets with our quantum classifier and compare its performance with respect to some classical machine learning classifiers. We also show that the number of training parameters in our classifier is significantly less than the classical classifiers.

On-chip learning in spin orbit torque driven domain wall synapse based crossbar fully connected neural network (FCNN) has been shown to be extremely efficient in terms of speed and energy, when compared to training on a conventional computing unit or even on a crossbar FCNN based on other non-volatile memory devices. However there are issues with respect to scalability of the on-chip learning scheme in the domain wall synapse based FCNN. Unless the scheme is scalable, it will not be competitive with respect to training a neural network on a conventional computing unit for real applications. In this paper, we have proposed a modification in the standard gradient descent algorithm, used for training such FCNN, by including appropriate thresholding units. This leads to optimization of the synapse cell at each intersection of the crossbars and makes the system scalable. In order for the system to approximate a wide range of functions for data classification, hidden layers must be present and the backpropagation algorithm (extension of gradient descent algorithm for multi-layered FCNN) for training must be implemented on hardware. We have carried this out in this paper by employing an extra crossbar. Through a combination of micromagnetic simulations and SPICE circuit simulations, we hence show highly improved accuracy for domain wall syanpse based FCNN with a hidden layer compared to that without a hidden layer for different machine learning datasets.