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Neuromorphic computing (NC) architecture has shown its suitability for energy-efficient computation. Amongst several systems, spin–orbit torque (SOT) based domain wall (DW) devices are one of the most energy-efficient contenders for NC. To realize spin-based NC architecture, the computing elements such as synthetic neurons and synapses need to be developed. However, there are very few experimental investigations on DW neurons and synapses. The present study demonstrates the energy-efficient operations of neurons and synapses by using novel reading and writing strategies. We have used a W/CoFeB-based energy-efficient SOT mechanism to drive the DWs at low current densities. We have used the concept of meander devices for achieving synaptic functions. By doing this, we have achieved 9 different resistive states in experiments. We have experimentally demonstrated the functional spike and step neurons. Additionally, we have engineered the anomalous Hall bars by incorporating several pairs, in comparison to conventional Hall crosses, to increase the sensitivity as well as signal-to-noise ratio (SNR). We have performed micromagnetic simulations and transport measurements to demonstrate the above-mentioned functionalities.

In alignment with the increasing demand for larger storage capacity and longer data retention, the electrical control of magnetic anisotropy has been a research focus in the realm of spintronics. Typically, magnetic anisotropy is determined by grain dimensionality, which is set during the fabrication of magnetic thin films. Despite the intrinsic correlation between magnetic anisotropy and grain dimensionality, there is a lack of experimental evidence for electrically controlling grain dimensionality, thereby impairing the efficiency of magnetic anisotropy modulation. Here, we demonstrate an electric field control of grain dimensionality and prove it as the active mechanism for tuning interfacial magnetism. The reduction in grain dimensionality is associated with a transition from ferromagnetic to superparamagnetic behavior. We achieve a nonvolatile and reversible modulation of the coercivity in both the ferromagnetic and superparamagnetic regimes. Subsequent electrical and elemental analysis confirms the variation in grain dimensionality upon the application of gate voltages, revealing a transition from a multidomain to a single-domain state, accompanied by a reduction in grain dimensionality. Furthermore, we exploit the influence of grain dimensionality on domain wall motion, extending its applicability to multilevel magnetic memory and synaptic devices. Our results provide a strategy for tuning interfacial magnetism through grain size engineering for advancements in high-performance spintronics.

Magnetization dynamics driven by an electric field could provide long-term benefits to information technologies because of its ultralow power consumption. Meanwhile, the Dzyaloshinskii-Moriya interaction in interfacially asymmetric multilayers consisting of ferromagnetic and heavy-metal layers can stabilize topological spin textures, such as chiral domain walls, skyrmions, and skyrmion bubbles. These topological spin textures can be controlled by an electric field and hold promise for building advanced spintronic devices. Here, we present an experimental and numerical study on the electric field-induced creation and directional motion of topological spin textures in magnetic multilayer films and racetracks with thickness gradient and interfacial Dzyaloshinskii-Moriya interaction at room temperature. We find that the electric field-induced directional motion of chiral domain wall is accompanied by the creation of skyrmion bubbles at certain conditions. We also demonstrate that the electric field variation can induce motion of skyrmion bubbles. Our findings may provide opportunities for developing skyrmion-based devices with ultralow power consumption.

ABSTRACTThirty children aged 3-10 years with clinically confirmed or suspected Duchenne Muscular Dystrophy (DMD) were analyzed for chromosomal aberrations using cytological preparations, biochemical changes using enzyme kit protocol, and deletions in the 26 exons of the DMDgene by targeting the mutations at the proximal and distal ‘hot spot’ regions of the dystrophin gene in South Indian patients with DMD. The frequenc y of chromosomal aberrations (both chromosomal and chromatid-type) and serum enzyme levels were significa ntly elevated in DMD subjects as compared to controls. Multiplex PCR assays revealed 27 patients having deletions in the DMDgene located at the distal ‘hot spot’ region. This study suggests that disease progression is directly associated with higher incidence of the deletions at the distal ‘hot spot’ of the DMDgene.

Neuromorphic computing (NC) is gaining wide acceptance as a potential technology to achieve low-power intelligent devices. To realize NC, researchers investigate various types of synthetic neurons and synaptic devices, such as memristors and spintronic devices. In comparison, spintronics-based neurons and synapses have potentially higher endurance. However, for realizing low-power devices, domain wall (DW) devices that show DW motion at low energies─typically below pJ/bit─are favored. Here, we demonstrate DW motion at current densities as low as 106A/m2by engineering the β-W spin–orbit coupling (SOC) material. With our design, we achieve ultralow pinning fields and current density reduction by a factor of 104. The energy required to move the DW by a distance of about 18.6 μm is 0.4 fJ, which translates into the energy consumption of 27 aJ/bit for a bit-length of 1 μm. With a meander DW device configuration, we have established a controlled DW motion for synapse applications and have shown the direction to make ultralow energy spin-based neuromorphic elements.

Neuromorphic computing (NC) is considered a potential vehicle for implementing energy-efficient artificial intelligence. To realize NC, several technologies are being investigated. Among them, the spin–orbit torque (SOT)-driven domain wall (DW) devices are one of the potential candidates. Researchers have proposed different device designs to achieve neurons and synapses, the building blocks of NC. However, the experimental realization of DW device-based NC is only at the primeval stage. Here, we have studied pine-tree DW devices, based on the Laplace pressure on the elastic DWs, for achieving synaptic functionalities and diode-like characteristics. We demonstrate an asymmetric pinning strength for DW motion in two opposite directions to show the potential of these devices as DW diodes. We have used micromagnetic simulations to understand the experimental findings and to estimate the Laplace pressure for various design parameters. The study provides a strategy to fabricate a multifunctional DW device, exhibiting synaptic properties and diode characteristics.

Neuromorphic computing (NC), which emulates neural activities of the human brain, is considered for the low-power implementation of artificial intelligence. Toward realizing NC, fabrication, and investigations of hardware elements─such as synaptic devices and neurons─are crucial. Electrolyte gating has been widely used for conductance modulation by massive carrier injections and has proven to be an effective way of emulating biological synapses. Synaptic devices, in the form of synaptic transistors, have been studied using various materials. Despite the remarkable progress, the study of metallic channel-based synaptic transistors remains massively unexplored. Here, we demonstrated a three-terminal electrolyte gating-modulated synaptic transistor based on a metallic cobalt thin film to emulate biological synapses. We have realized gating-controlled, non-volatile, and distinct multilevel conductance states in the proposed device. The essential synaptic functions demonstrating both short-term and long-term plasticity have been emulated in the synaptic device. A transition from short-term to long-term memory has been realized by tuning the gate pulse parameters, such as amplitude and duration. The crucial cognitive behavior, including learning, forgetting, and re-learning, has been emulated, showing a resemblance to the human brain. Beyond that, dynamic filtering behavior has been experimentally implemented in the synaptic device. These results provide an insight into the design of metallic channel-based synaptic transistors for NC.

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