26 research outputs found

    All Spin Logic device with inbuilt Non-Reciprocity

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    The need for low power alternatives to digital electronic circuits has led to increasing interest in logic devices where information is stored in nanomagnets. This includes both nanomagnetic logic (NML) where information is communicated through magnetic fields of nanomagnets and all-spin logic (ASL) where information is communicated through spin currents. A key feature needed for logic implementation is non-reciprocity, whereby the output is switched according to the input but not the other way around, thus providing directed information transfer. The objective of this paper is to draw attention to possible ASL-based schemes that utilize the physics of spin-torque to build in non-reciprocity similar to transistors that could allow logic implementation without the need for special clocking schemes. We use an experimentally benchmarked coupled spin-transport/ magnetization-dynamics model to show that a suitably engineered single ASL unit indeed switches in a non-reciprocal manner. We then present heuristic arguments explaining the origin of this directed information transfer. Finally we present simulations showing that individual ASL devices with inbuilt directionality can be cascaded to construct circuits.Comment: 7 pages, 8 figures, To appear in IEEE Trans. Mag

    Semi-automated feature extraction from RGB images for sorghum panicle architecture GWAS

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    Because structural variation in the inflorescence architecture of cereal crops can influence yield, it is of interest to identify the genes responsible for this variation. However, the manual collection of inflorescence phenotypes can be time-consuming for the large populations needed to conduct GWAS (genome-wide association studies) and is difficult for multi-dimensional traits such as volume. A semi-automated phenotyping pipeline (Toolkit for Inflorescence Measurement, TIM) was developed and used to extract uni- and multi-dimensional features from images of 1,064 sorghum (Sorghum bicolor) panicles from 272 genotypes comprising a subset of the Sorghum Association Panel (SAP). GWAS detected 35 unique SNPs associated with variation in inflorescence architecture. The accuracy of the TIM pipeline is supported by the fact that several of these trait-associated SNPs (TASs) are located within chromosomal regions associated with similar traits in previously published QTL and GWAS analysis of sorghum. Additionally, sorghum homologs of maize (Zea mays) and rice (Oryza sativa) genes known to affect inflorescence architecture are enriched in the vicinities of TASs. Finally, our TASs are enriched within genomic regions that exhibit high levels of divergence between converted tropical lines and cultivars, consistent with the hypothesis that these chromosomal intervals were targets of selection during modern breeding

    The effect of artificial selection on phenotypic plasticity in maize

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    Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements

    All spin logic: Modeling multi-magnet networks interacting via spin currents

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    The increasing level of power dissipation in today\u27s transistors, due to their continued downscaling, has led to an interest in alternatives to charge-based electronics for information processing. All-spin logic (ASL) represents one such new approach where the roles of charges and capacitors in CMOS are now played by spins and magnets. Available experiments utilizing this principle show operating voltages of the order of few tens of milli-volts, far below today\u27s transistors. However, before an alternative logic scheme — like ASL — can be employed to build logic circuits, certain characteristics have to first be exhibited at the device level such as directionality of information transfer, implementing universal logic gates, cascading and fan-out. In order to devise and analyze ASL based strategies that can incorporate these device characteristics, this report first introduces a novel 4-component Spin-Circuit formalism, which is then coupled to an existing model for magnetization dynamics. This coupled model can simultaneously describe two distinct physical phenomena: (1) spin torque switching of magnets and (2) generation and transport of non-collinear spin currents in spin diffusive channels. The model is first benchmarked against available experimental data and is then used to provide key insights at the ASL device level, such as how to incorporate inbuilt directionality of information transfer and to propose scaling laws. Towards the end of this report, the model is extended to simulate multi-magnet ASL networks interacting via spin currents. In particular, examples of an ASL ring oscillator and a universal NAND gate are presented, which form the basis for designing large scale ASL circuits

    “Property Phase Diagrams” for Compound Semiconductors through Data Mining

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    This paper highlights the capability of materials informatics to recreate “property phase diagrams” from an elemental level using electronic and crystal structure properties. A judicious selection of existing data mining techniques, such as Principal Component Analysis, Partial Least Squares Regression, and Correlated Function Expansion, are linked synergistically to predict bandgap and lattice parameters for different stoichiometries of GaxIn1−xAsySb1−y, starting from fundamental elemental descriptors. In particular, five such elemental descriptors, extracted from within a database of highly correlated descriptors, are shown to collectively capture the widely studied “bowing” of energy bandgaps seen in compound semiconductors. This is the first such demonstration, to our knowledge, of establishing relationship between discrete elemental descriptors and bandgap bowing, whose underpinning lies in the fundamentals of solid solution thermodyanamics

    MG-R identification of component boundaries

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