Srikant Srinivasan

Switching energy-delay of all spin logic devices

A recent proposal called all spin logic (ASL) proposes to store information in nanomagnets that communicate with spin currents in order to construct spin based digital circuits. We present a coupled magnetodynamics/spin-transport model for ASL devices that is based on established physics and is benchmarked against available experimental data. This model is used to show the linear dependence of switching energy and quadratic dependence of energy-delay of ASL devices on the number of Bohr magnetons comprising a nanomagnet. A scaling scheme that could lower the energy-delay of spin-torque switching while maintaining thermal stability is discussed.

Low-power functionality enhanced computation architecture using spin-based devices

Power consumption in CMOS integrated circuits increases every technology generation due to increased subthreshold and gate leakage currents. To cope with such a problem, researchers have started looking at the possibility of logic devices based on electron spin, as an alternative to charge based CMOS, for realizing low-power integrated circuits with low active power dissipation and zero standby leakage. In this paper, we investigate spin-based logic devices that employ low-power spin-torque switching mechanism for circuit operation. We have developed a Functionality Enhanced All Spin Logic (FEASL) architecture and a synthesis framework using Logically Passively Self Dual (LPSD) formulation. This methodology enables the design of large functional logic blocks, especially low-power adders and multipliers, which constitute the building blocks of all arithmetic logic units (ALU). In addition, we have investigated three different variants of ASL, which are low-power, medium-power—medium performance and high performance and we analyze their merits and drawbacks at circuit/architecture level. We synthesized Discrete Cosine Transform (DCT) algorithm using adders and multipliers to show the efficacy of the proposed FEASL approach in designing digital signal processing (DSP) systems. Compared to 15nm CMOS implementation, the FEASL based DCT shows 88% improvement in power and 83% in PDP with 43% degradation in performance.

All-Spin Logic Device With Inbuilt Nonreciprocity

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, 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 nonreciprocity, 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 nonreciprocity, as in 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 nonreciprocal manner. We then present heuristic arguments explaining the origin of this directed information transfer. Finally, we present simulations showing that individual ASL devices can be cascaded to construct a ring oscillator circuit, which provides a clear signature of inbuilt directionality.

Numerical analysis of domain wall propagation for dense memory arrays

This paper presents numerical analysis of domain wall propagation for dense embedded memory applications. Self-consistent simulation framework based on Four Component Spin Transport Model and Landau-Lifshitz-Gilbert equation is able to capture domain wall motion in terms of critical current density requirement, domain wall velocity, and power dissipation. Effect of patterned notches on memory stability, domain wall velocity and nanostrip resistance are also presented. Finally, the proposed simulation framework is used to investigate performance, scalability and organization of the domain wall motion based memory structure.

Property Phase Diagrams for Compound Semiconductors through Data Mining

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.

An informatics based analysis of the impact of isotope substitution on phonon modes in graphene

It is shown by informatics that the high frequency short ranged modes exert a significant influence in impeding thermal transport through isotope substituted graphene nanoribbons. Using eigenvalue decomposition methods, we have extracted features in the phonon density of states spectra that reveal correlations between isotope substitution and phonon modes. This study also provides a data driven computational framework for the linking of materials chemistry and transport properties in 2D systems.

Modeling all spin logic: Multi-magnet networks interacting via spin currents

All-spin logic (ASL) represents a new approach to information processing where the roles of charges and capacitors in CMOS are played by spins and magnets. This paper (1) summarizes our earlier work on the input-output isolation and intrinsic directivity of ASL devices, (2) uses an experimentally benchmarked simulator for multimagnet networks coupled by spin transport channels to demonstrate a combinational NAND gate, and (3) describes the natural mapping of such ASL networks into neuromorphic circuits suitable for hybrid analog/digital information processing.

Mapping Chemical Selection Pathways for Designing Multicomponent Alloys: an informatics framework for materials design

A data driven methodology is developed for tracking the collective influence of the multiple attributes of alloying elements on both thermodynamic and mechanical properties of metal alloys. Cobalt-based superalloys are used as a template to demonstrate the approach. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can guide targeted first principles calculations that identify the influence of specific elements on phase stability, crystal structure and elastic properties. This provides a fundamentally new means to rapidly identify new stable alloy chemistries with enhanced high temperature properties. The resulting visualization scheme exhibits the grouping and proximity of elements based on their impact on the properties of intermetallic alloys. Unlike the periodic table however, the distance between neighboring elements uncovers relationships in a complex high dimensional information space that would not have been easily seen otherwise. The predictions of the methodology are found to be consistent with reported experimental and theoretical studies. The informatics based methodology presented in this study can be generalized to a framework for data analysis and knowledge discovery that can be applied to many material systems and recreated for different design objectives.

Valley splitting in Si quantum dots embedded in SiGe

We examine energy spectra of Si quantum dots embedded in Si0.75Ge0.25 buffers using atomistic numerical calculations for dimensions relevant to qubit implementations. The valley degeneracy of the lowest orbital state is lifted and valley splitting fluctuates with monolayer frequency as a function of the dot thickness. For dot thicknesses ≤6 nm, valley splitting is found to be >150 μeV. Using the unique advantage of atomistic calculations, we analyze the effect of buffer disorder on valley splitting. Disorder in the buffer leads to the suppression of valley splitting by a factor of 2.5; the splitting fluctuates with ≈20 μeV for different disorder realizations. Through these simulations we can guide future experiments into regions of low device-to-device fluctuations.

Distinct genetic architectures for phenotype means and plasticities in Zea mays

Phenotypic plasticity describes the phenotypic variation of a trait when a genotype is exposed to different environments. Understanding the genetic control of phenotypic plasticity in crops such as maize is of paramount importance for maintaining and increasing yields in a world experiencing climate change. Here, we report the results of genome-wide association analyses of multiple phenotypes and two measures of phenotypic plasticity in a maize nested association mapping (US-NAM) population grown in multiple environments and genotyped with ~2.5 million single-nucleotide polymorphisms. We show that across all traits the candidate genes for mean phenotype values and plasticity measures form structurally and functionally distinct groups. Such independent genetic control suggests that breeders will be able to select semi-independently for mean phenotype values and plasticity, thereby generating varieties with both high mean phenotype values and levels of plasticity that are appropriate for the target performance environments.Whether phenotypic mean values and plasticity share similar genetic architectures remains elusive. A study examining multiple traits in a maize NAM population using GWAS showed that genes underlying mean and plasticity measures form distinct groups.