Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence.

Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the "non-ideal" behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.

Local transport properties, morphology and microstructure of ZnO decorated SiO2 nanoparticles.

We report on a novel, surfactant free method for achieving nanocrystalline ZnO decoration of an SiO(2) nanoparticle at ambient temperature. The size distributions of the naked and decorated SiO(2) nanoparticles are measured by means of dynamic light scattering, and a monodisperse distribution is observed for each. The morphology and microstructure of the nanoparticles are explored using atomic force microscopy and high resolution transmission electron microscopy. Investigation of the optical properties of the ZnO decorated SiO(2) nanoparticles shows absorption at 350 nm. This blue shift in absorption as compared to bulk ZnO is shown to be consistent with quantum confinement effects due to the small size of the ZnO nanocrystals. Finally, the local electronic transport properties of the nanoparticles are explored by scanning conductance atomic force microscopy. A memristive hysteresis in the transport properties of the individual ZnO decorated SiO(2) nanoparticles is observed. Optical absorption measurements suggest the presence of oxygen vacancies, whose migration and annihilation appear to contribute to the dynamic conduction properties of the ZnO decorated nanoparticles. We believe this to be the first demonstration of a ZnO decorated SiO(2) nanoparticle, and this represents a simple yet powerful way of achieving the optical and electrical properties of ZnO in combination with the simplicity of SiO(2) synthesis.