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Modulation of stress anisotropy of magnetostrictive nanomagnets with strain offers an extremely energy-efficient method of magnetization reversal. The reversal process, however, is often incoherent and hence, error-prone in the presence of thermal noise at room temperature. Occurrence of incoherent metastable states in the potential landscape of the nanomagnet can further exacerbate the error. Stochastic micromagnetic simulations at room temperature are used to understand and calculate energy dissipations and switching error probabilities in this important magnetization switching methodology. We find that these quantities have an intriguing dependence on nanomagnet size. Small nanomagnets perform better owing to the fact that they are more resilient to the formation of metastable states and magnetization dynamics in them is more coherent. However, for a fixed stress anisotropy energy density, smaller nanomagnets will also have poorer resilience against thermal instability. Thus, the challenge in straintronics is to maximize the stress anisotropy energy density by developing materials and processes that yield the largest magnetostriction. [ABSTRACT FROM PUBLISHER]

Strain-clocked dipole-coupled nanomagnetic logic (DC-NML) is an energy-efficient Boolean logic paradigm whose progress has been stymied by its propensity for high error rates. In an effort to mitigate this problem, we have studied the effect of nanomagnet geometry on error rates, focusing on elliptical and cylindrical geometries. We had previously reported that in elliptical nanomagnets, the out-of-plane excursion of the magnetization vector during switching creates a precessional torque that plays a dual role—it speeds up the switching, but is also responsible for the high switching error probability. The absence of this torque in cylindrical magnets should lower error rates, but our simulations show that the error rate actually does not improve significantly compared with elliptical magnets while the switching becomes unacceptably slow. Here, we show that DC-NML employing elliptical nanomagnets can offer relatively high reliability for NML (switching error probability < 10^-8 ), moderate clock speed ( $\sim 100$ MHz), and two to three orders of magnitude energy saving compared with CMOS devices, provided the shape anisotropy energy barrier of the nanomagnet is increased to at least $\sim 5.5$ eV to allow engineering a stronger dipole coupling between neighboring nanomagnets. [ABSTRACT FROM AUTHOR]

Hardware-based image processing offers speed and convenience not found in software-centric approaches. Here, we show theoretically that a 2-D periodic array of dipole-coupled elliptical nanomagnets, delineated on a piezoelectric substrate, can act as a dynamical system for specific image processing functions. Each nanomagnet has two stable magnetization states that encode pixel color (black or white). An image containing black and white pixels is first converted to voltage states and then mapped into the magnetization states of a nanomagnet array with magneto-tunneling junctions (MTJs). The same MTJs are employed to read out the processed pixel colors later. Dipole interaction between the nanomagnets implements specific image processing tasks, such as noise reduction and edge enhancement detection. These functions are triggered by applying a global strain to the nanomagnets with a voltage dropped across the piezoelectric substrate. An image containing an arbitrary number of black and white pixels can be processed in few nanoseconds with very low energy cost. [ABSTRACT FROM AUTHOR]