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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. 

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. 

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.