Multiple Avalanches Across the Metal-Insulator Transition of Vanadium Oxide Nano-scaled Junctions

The metal insulator transition of nano-scaled $VO_2$ devices is drastically different from the smooth transport curves generally reported. The temperature driven transition occurs through a series of resistance jumps ranging over 2 decades in amplitude, indicating that the transition is caused by avalanches. We find a power law distribution of the jump amplitudes, demonstrating an inherent property of the $VO_2$ films. We report a surprising relation between jump amplitude and device size. A percolation model captures the general transport behavior, but cannot account for the statistical behavior.Comment: 4 papers and 4 figures submitted to PR

A caloritronics-based Mott neuristor

Machine learning imitates the basic features of biological neural networks to efficiently perform tasks such as pattern recognition. This has been mostly achieved at a software level, and a strong effort is currently being made to mimic neurons and synapses with hardware components, an approach known as neuromorphic computing. CMOS-based circuits have been used for this purpose, but they are non-scalable, limiting the device density and motivating the search for neuromorphic materials. While recent advances in resistive switching have provided a path to emulate synapses at the 10 nm scale, a scalable neuron analogue is yet to be found. Here, we show how heat transfer can be utilized to mimic neuron functionalities in Mott nanodevices. We use the Joule heating created by current spikes to trigger the insulator-to-metal transition in a biased VO2 nanogap. We show that thermal dynamics allow the implementation of the basic neuron functionalities: activity, leaky integrate-and-fire, volatility and rate coding. By using local temperature as the internal variable, we avoid the need of external capacitors, which reduces neuristor size by several orders of magnitude. This approach could enable neuromorphic hardware to take full advantage of the rapid advances in memristive synapses, allowing for much denser and complex neural networks. More generally, we show that heat dissipation is not always an undesirable effect: it can perform computing tasks if properly engineered

Deconvoluting Reversal Modes in Exchange Biased Nanodots

Ensemble-averaged exchange bias in arrays of Fe/FeF2 nanodots has been deconvoluted into local, microscopic, bias separately experienced by nanodots going through different reversal modes. The relative fraction of dots in each mode can be modified by exchange bias. Single domain dots exhibit a simple loop shift, while vortex state dots have asymmetric shifts in the vortex nucleation and annihilation fields, manifesting local incomplete domain walls in these nanodots as magnetic vortices with tilted cores.Comment: 17 pages, 3 figures. Phys. Rev. B in pres

Acquisition of an In-House X-ray Scattering Facility for Nanostructure Characterization and Student Training

This equipment grant was specifically dedicated to the development of a "state of the art" x-ray scattering facility..

Dynamics of Spontaneous Magnetization Reversal in Exchange Biased Heterostructures

The dependence of thermally induced spontaneous magnetization reversal on time-dependent cooling protocols was studied. Slower cooling and longer waiting close to the N\`{e}el temperature of the antiferromagnet ($T_N$) enhances the magnetization reversal. Cycling the temperature around $T_N$ leads to a thermal training effect under which the reversal magnitude increases with each cycle. These results suggest that spontaneous magnetization reversal is energetically favored, contrary to our present understanding of positive exchange bias