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Voltage-tuning of magnetic anisotropy is demonstrated in ferrimagnetic insulating rare earth iron garnets on a piezoelectric substrate, (011)-oriented PMN-PT. A 42 nm thick yttrium-substituted dysprosium iron garnet (YDyIG) film is grown via pulsed laser deposition followed by a rapid thermal anneal to crystallize the garnet into ≈5  μm diameter grains. The annealed polycrystalline film is magnetically isotropic in the film plane with total anisotropy dominated by shape and magnetoelastic contributions. Application of an electric field perpendicular to the substrate breaks the in-plane easy axis along [01[Formula: see text]] and an intermediate axis along [100]. The results are explained in terms of the piezoelectric remanent strain caused by poling the substrate, which is transferred to the YDyIG and modulates the magnetoelastic anisotropy. 

We demonstrate using micromagnetic simulations that a nanomagnet array excited by surface acoustic waves (SAWs) can work as a reservoir. An input nanomagnet is excited with focused SAW and coupled to several nanomagnets, seven of which serve as output nanomagnets. To evaluate memory effect and computing capability, we study the short-term memory (STM) and parity check (PC) capacities, respectively. The SAW (4 GHz carrier frequency) amplitude is modulated to provide a sequence of sine and square waves of 100 MHz frequency. The responses of the selected output nanomagnets are processed by reading the envelope of their magnetization states, which is used to train the output weights using the regression method. For classification, a random sequence of 100 square and sine wave samples is used, of which 80% are used for training, and the rest are used for testing. We achieve 100% training and 100% testing accuracy. The average STM and PC are calculated to be ∼4.69 and ∼5.39 bits, respectively, which is indicative of the proposed acoustically driven nanomagnet oscillator array being well suited for physical reservoir computing applications. The energy dissipation is ∼2.5 times lower than a CMOS-based echo-state network. Furthermore, the reservoir is able to accurately predict Mackey-Glass time series up to several time steps ahead. Finally, the ability to use high frequency SAW makes the nanomagnet reservoir scalable to small dimensions, and the ability to modulate the envelope at a lower frequency (100 MHz) adds flexibility to encode different signals beyond the sine/square waves classification and Mackey-Glass predication tasks demonstrated here. 

Abstract Implementation of skyrmion based energy efficient and high-density data storage devices requires aggressive scaling of skyrmion size. Ferrimagnetic materials are considered to be a suitable platform for this purpose due to their low saturation magnetization (i.e. smaller stray field). However, this method of lowering the saturation magnetization and scaling the lateral size of skyrmions is only applicable where the skyrmions have a smaller lateral dimension compared to the hosting film. Here, we show by performing rigorous micromagnetic simulation that the size of skyrmions, which have lateral dimension comparable to their hosting nanodot can be scaled by increasing saturation magnetization. Also, when the lateral dimension of nanodot is reduced and thereby the skyrmion confined in it is downscaled, there remains a challenge in forming a stable skyrmion with experimentally observed Dzyaloshinskii–Moriya interaction (DMI) values since this interaction has to facilitate higher canting  per spin to complete a 360° rotation along the diameter. In our study, we found that skyrmions can be formed in 20 nm lateral dimension nanodots with high saturation magnetization (1.30–1.70 MA/m) and DMI values (~ 3 mJ/m 2 ) that have been reported to date. This result could stimulate experiments on implementation of highly dense skyrmion devices. Additionally, using this, we show that voltage controlled magnetic anisotropy based switching mediated by an intermediate skyrmion state can be achieved in the soft layer of a ferromagnetic p-MTJ of lateral dimensions 20 nm with sub 1 fJ/bit energy in the presence of room temperature thermal noise with reasonable DMI ~ 3 mJ/m 2 . 

We propose energy-efficient voltage-induced strain control of a domain wall (DW) in a perpendicularly magnetized nanoscale racetrack on a piezoelectric substrate that can implement a multistate synapse to be utilized in neuromorphic computing platforms. Here, strain generated in the piezoelectric is mechanically transferred to the racetrack and modulates the perpendicular magnetic anisotropy (PMA) in a system that has significant interfacial Dzyaloshinskii-Moriya interaction (DMI). When different voltages are applied (i.e., different strains are generated) in conjunction with spin-orbit torque (SOT) due to a fixed current flowing in the heavy metal layer for a fixed time, DWs are translated to different distances and implement different synaptic weights. We have shown using micromagnetic simulations that five-state and three-state synapses can be implemented in a racetrack that is modeled with the inclusion of natural edge roughness and room temperature thermal noise. These simulations show interesting dynamics of DWs due to interaction with roughness-induced pinning sites. Thus, notches need not be fabricated to implement multistate nonvolatile synapses. Such a strain-controlled synapse has an energy consumption of ~1 fJ and could thus be very attractive to implement energy-efficient quantized neural networks, which has been shown recently to achieve near equivalent classification accuracy to the full-precision neural networks. 

Prevention of integrated circuit counterfeiting through logic locking faces the fundamental challenge of securing an obfuscation key against both physical and algorithmic threats. Previous work has focused on strengthening the logic encryption to protect the key against algorithmic attacks, but failed to provide adequate physical security. In this work, we propose a logic locking scheme that leverages the non-volatility of the nanomagnet logic (NML) family to achieve both physical and algorithmic security. Polymorphic NML minority gates protect the obfuscation key against algorithmic attacks, while a strain-inducing shield surrounding the nanomagnets provides physical security via a self-destruction mechanism.