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The surface state of a 3D topological insulator (3DTI) is a spin-momentum locked conductive state, whose large spin hall angle can be used for the energy-efficient spin orbit torque based switching of an overlying ferromagnet (FM). Conversely, the gated switching of the magnetization of a separate FM in or out of the TI surface plane, can turn on and off the TI surface current. The gate tunability of the TI Dirac cone gap helps reduce its sub-threshold swing. By exploiting this reciprocal behaviour, we can use two FM/3DTI heterostructures to design a 1-Transistor 1-magnetic tunnel junction random access memory unit (1T1MTJ RAM) for an ultra low power Processing-in-Memory (PiM) architecture. Our calculation involves combining the Fokker-Planck equation with the Non-equilibrium Green Function (NEGF) based flow of conduction electrons and Landau-Lifshitz-Gilbert (LLG) based dynamics of magnetization. Our combined approach allows us to connect device performance metrics with underlying material parameters, which can guide proposed experimental and fabrication efforts., 5 pages, 4 figures

We present a low barrier magnet based compact hardware unit for analog stochastic neurons and demonstrate its use as a building-block for neuromorphic hardware. By coupling circular magnetic tunnel junctions (MTJs) with a CMOS based analog buffer, we show that these units can act as leaky-integrate-and fire (LIF) neurons, a model of biological neural networks particularly suited for temporal inferencing and pattern recognition. We demonstrate examples of temporal sequence learning, processing, and prediction tasks in real time, as a proof of concept demonstration of scalable and adaptive signal-processors. Efficient non von-Neumann hardware implementation of such processors can open up a pathway for integration of hardware based cognition in a wide variety of emerging systems such as IoT, industrial controls, bio- and photo-sensors, and Unmanned Autonomous Vehicles., 4 pages, 4 figures, under review

Race logic is a relative timing code that represents information in a wavefront of digital edges on a set of wires in order to accelerate dynamic programming and machine learning algorithms. Skyrmions, bubbles, and domain walls are mobile magnetic configurations (solitons) with applications for Boolean data storage. We propose to use current-induced displacement of these solitons on magnetic racetracks as a native temporal memory for race logic computing. Locally synchronized racetracks can spatially store relative timings of digital edges and provide non-destructive read-out. The linear kinematics of skyrmion motion, the tunability and low-voltage asynchronous operation of the proposed device, and the elimination of any need for constant skyrmion nucleation make these magnetic racetracks a natural memory for low-power, high-throughput race logic applications., Comment: 9 pages, 3 figures, submitted for review

High-performance graphene-HgCdTe detector technology has been developed combining the best properties of both materials for mid-wave infrared (MWIR) detection and imaging. The graphene functions as a high mobility channel that whisks away carriers before they can recombine, further contributing to detection performance. Comprehensive modeling on the HgCdTe, graphene, and the HgCdTe-graphene interface has aided the design and development of this MWIR detector technology. Chemical doping of the bilayer graphene lattice has enabled p-type doping levels in graphene for high mobility implementation in high-performance MWIR HgCdTe detectors. Characterization techniques, including SIMS and XPS, confirm high boron doping concentrations. A spin-on doping (SOD) procedure is outlined that has provided a means of doping layers of graphene on native substrates, while subsequently allowing integration of the doped graphene layers with HgCdTe for final implementation in the MWIR photodetection devices. Successful integration of graphene into HgCdTe photodetectors can thus provide higher MWIR detector efficiency and performance compared to HgCdTe-only detectors. New earth observation measurement capabilities are further enabled by the room temperature operational capability of the graphene-enhanced HgCdTe detectors and arrays to benefit and advance space and terrestrial applications.

Infrared detectors and focal plane array technologies are becoming ubiquitous in military, but are limited in the commercial sectors. The widespread commercial use of this technology is lacking because of the high cost and large size, weight and power. Most of these detectors require cryogenic cooling to minimize thermally generated dark currents, causing the size, weight, power and cost to increase significantly. Approaches using very thin detector design can minimize thermally generated dark current, but at a cost of lower absorption efficiency. There are emerging technologies in nanostructured material designs such as metasurfaces that can allow for increased photon absorption in a thin detector architecture. Ultra-thin and low-dimensional absorber materials may also provide unique engineering opportunities in detector design. This chapter discusses the physics and opportunities to increase the operating temperature using such techniques.

A P-N junction engineered within a Dirac cone system acts as a gate tunable angular filter based on Klein tunneling. For a 3D topological insulator with a substantial bandgap, such a filter can produce a charge-to-spin conversion due to the dual effects of spin-momentum locking and momentum filtering. We analyze how spins filtered at an in-plane topological insulator PN junction (TIPNJ) interact with a nanomagnet, and argue that the intrinsic charge-to-spin conversion does not translate to an external gain if the nanomagnet also acts as the source contact. Regardless of the nanomagnet's position, the spin torque generated on the TIPNJ is limited by its surface current density, which in turn is limited by the bulk bandgap. Using quantum kinetic models, we calculated the spatially varying spin potential and quantified the localization of the current versus the applied bias. Additionally, with the magnetodynamic simulation of a soft magnet, we show that the PN junction can offer a critical gate tunability in the switching probability of the nanomagnet, with potential applications in probabilistic neuromorphic computing.

Majorana bound states have been proposed as a candidate for topological quantum computing (QC), a QC paradigm where qubits are topologically protected against dephasing compared to non-topological QC. This can enable low error QC where the number of physical q-bits required is significantly smaller. N-S-N nanowires (Kitaev Chain) have been proposed as a device for topological QC. In this work, we build a Non-Equilibrium Greens Function (NEGF) based simulation platform that incorporates elastic dephasing using the self-consistent Born approximation (SCBA) and find Majorana modes to be robust against dephasing. We also compare the calculated conductance of the N-S-N system as a function of energy and applied magnetic field with predictions in the literature.

Magnetic Topological Textures (MTTs) have the potential to become the building block of future memory and logic-in-memory devices due to their compact size and non-volatility. However, these MTTs are used in conjunction with the CMOS control circuit to design memory and logic-in-memory devices. Micromagnetic and SPICE simulations need to be carried out separately for MTT and CMOS, respectively, to evaluate the performance of these hybrid devices. Thus, a SPICE model that can emulate the physical behavior of the MTT would allow the designers to evaluate the performance of these hybrid devices accurately and enable design space exploration. In this paper, we build a compact and modular SPICE model of MTT nucleation, motion, detection, and annihilation on the racetrack by extracting parameters from micromagnetic simulation tools. We also build a CMOS-MTT generic racetrack circuit model in SPICE and validate the model’s accuracy by comparing it with micromagnetic simulation results. As we can perform the simulations fully within SPICE while capturing the essential physics of the MTTs on the racetrack, we enable simulation runtime acceleration by a factor of > 103 and > 106 times approximately compared to the micromagnetic simulations carried out with MUMAX3 and OOMMF. We envision that the proposed model will enable design and application explorations and device-circuit co-simulation, co-design, and co-optimization for the emerging MTT-based computing systems.

High performance detector technology is being developed for sensing over the mid-wave infrared (MWIR) band for NASA Earth Science, defense, and commercial applications. The graphene-based HgCdTe detector technology involves integration of graphene with HgCdTe photodetectors allowing higher performance detection over 2-5 μm compared with photodetectors using only HgCdTe material. The graphene layer functioning as a high mobility channel reduces recombination of photogenerated carriers in the detector to further enhance performance. Graphene bilayers on Si/SiO2 substrates have been doped with boron using a spin-on dopant (SOD) process. The p-doped graphene is then transferred onto HgCdTe substrates for high mobility layers in MWIR photodetectors. Various characterization techniques including Raman spectroscopy and secondary-ion mass spectroscopy (SIMS) have analyzed dopant levels and properties of the graphene throughout various stages of development to qualify and quantify the graphene doping and transfer. The objective of this work is demonstration of graphene-based HgCdTe room temperature MWIR detectors and arrays through modeling, material development, and device optimization. The primary driver for this technology development is enablement of a scalable, low cost, low power, and small footprint uncooled MWIR sensing technology capable of measuring thermal dynamics with better spatial resolution for applications such as remote sensing and earth observation.

Recent experimental investigations in Bi-Layer Graphene (BLG) have shown interesting and useful electrical behavior that can be leveraged in building novel device applications. One such behavior being carrier multiplication effects within the BLG. We propose to use a heterostructure of BLG-CdTe-HgCdTe as a detector structure that marries this effect along with the unique bandalignment of the heterostructure which suggest an Ohmic contact for the electrons but a Schottky barrier for the holes. Using a dissipative quantum transport model, namely the Non-Equilibrium Green’s Functions with self-consistent Born approximation accounting for electron-electron and electron-phonon interactions, we calculate energy and position resolved electron and hole currents through these heterostructure and discover a 20X ratio of electron-to-hole ratio in an illustrative simulation which demonstrates the potential for reducing the hole contribution to dark current and modulating the recombination mechanisms and therefore the lifetime of the carriers in the heterostructure.

Carrier sweepout is one of the many phenomena that limit critical performance metrics of mid-wave infrared (MWIR) photodetectors, such as the photoconductive gain (γ), photoresponsivity (η), specific detectivity (D*), and hence the overall performance of cameras built using these detectors. Preventing carrier sweepout in photoconductors at high applied bias and modulation frequencies can increase the electrical operating bias range and consequently expand the possible read-out integrated circuit design space and capabilities. Polycrystalline PbSe-based MWIR photodetectors have shown great potential for building integrated high-performance devices. We discuss herein the choice of metallic contacts for such detectors built by Northrop Grumman Systems Corp. under this program using the complex physics of carrier trapping and the interface of the contact metal with the photosensitive PbSe film that allows these detectors to be essentially free of carrier sweepout even at high applied electric fields.

III-V material based digital alloy Avalanche Photodiodes (APDs) have recently been found to exhibit low noise similar to Silicon APDs. The III-V materials can be chosen to operate at any wavelength in the infrared spectrum. In this work, we present a physics-based SPICE compatible compact model for APDs built from parameters extracted from an Environment-Dependent Tight Binding (EDTB) model calibrated to ab-initio Density Functional Theory (DFT) and Monte Carlo (MC) methods. Using this approach, we can accurately capture the physical characteristics of these APDs in integrated photonics circuit simulations., 8 pages, 11 figures

Spiking neural networks, inspired by biological neural systems, could process immense volumes of spatio-temporal data by representing them as spikes. Here, we propose to implement compact, scalable, energy-efficient spiking neurons based on the unique insulator-metal transition in Vanadium dioxide $(VO_{2})$ which interact through memristive synapses, to emulate a Liquid State Machine (LSM). Further, we demonstrate the implementation of this recurrent neural network as a temporal auto-encoder, and adaptive channel equalizer for application in neuromorphic signal processing. Our approach provides a pathway to reduce component count $(50\ -100X)$ and improve energy efficiency $(> 50X)$ over conventional CMOS based implementations.

Magnetic skyrmions are quasiparticle configurations in a magnetic film that can act as information carrying bits for ultrasmall, low power nonvolatile memory. Skyrmions can be nucleated and driven by spin-orbit torque from a current driven in a heavy metal underlying a ferromagnetic layer, the configuration commonly called a racetrack. Recently it has been shown that by hybridizing the skyrmion between Neel and Bloch types the magnus effect on skyrmion motion, which makes it veer from a straight path down a racetrack, can be effectively canceled which provides them a self-focused naturally converging lane" to travel through. This is achieved by exploiting the voltage controlled magnetic anisotropy effect whereby the magnetic anisotropy of the ferromagnetic racetrack can be positionally modulated by a gate voltage. In this work we show, using detailed micromagnetic simulations, that by using hybrid skyrmions we can obtain demultiplexer functionality out of a racetrack. We further propose a hybrid skyrmionic reconfigureable computing fabric. In conventional CMOS based field programmable gate arrays, SRAM cells are used to build a LUTs storing pre-computed truth-table of a Boolean function and a multiplexer selects one of the storage cells as the output. We show that non-volatile hybrid skyrmions can also act as the memory element and the gateable self-focused nature of the hybrid skyrmions can be exploited to program the proposed CMOS-skyrmion hybrid design to perform different logic operations. The low driving energy and non-volatility of magnetic skyrmion in a racetrack promises the development of energy efficient programmable architecture for future system-on-a-Chip (SoC) designs.

Biologically inspired recurrent neural networks, such as reservoir computers are of interest in designing spatio-temporal data processors from a hardware point of view due to the simple learning scheme and deep connections to Kalman filters. In this work we discuss using in-depth simulation studies a way to construct hardware reservoir computers using an analog stochastic neuron cell built from a low energy-barrier magnet based magnetic tunnel junction and a few transistors. This allows us to implement a physical embodiment of the mathematical model of reservoir computers. Compact implementation of reservoir computers using such devices may enable building compact, energy-efficient signal processors for standalone or in-situ machine cognition in edge devices., To be presented at International Conference on Neuromorphic Systems 2020

With recent developments in spintronics, it is now possible to envision “spin-driven” devices with magnets and interconnects that require a new class of transport models using generalized Fermi functions and currents, each with four components: one for charge and three for spin. The corresponding impedance elements are not pure numbers but $\text{4}\times \text{4}$ matrices. Starting from the Non-Equilibrium Green's Function formalism in the elastic, phase-coherent transport regime, we develop spin generalized Landauer-Buttiker formulas involving such $\text{4}\times \text{4}$ conductances, for multi-terminal devices in the presence of normal-metal leads. In addition to usual “terminal” conductances describing currents at the contacts, we provide “spin-transfer torque” conductances describing the spin currents absorbed by ferromagnetic regions inside the conductor, specifying both of these currents in terms of Fermi functions at the terminals. We derive universal sum rules and reciprocity relations that would be obeyed by such matrix conductances. Finally, we apply our formulation to two example Hamiltonians describing the Rashba and the Hanle effect in two-dimensional. Our results allow the use of pure quantum transport models as building blocks in constructing circuit models for complex spintronic and nano-magnetic structures and devices for simulation in SPICE-like simulators.

Graphene-HgCdTe heterostructure based mid wave IR (MWIR) detectors are being designed for NASA Earth Science applications. Combining Density Functional Theory (DFT) based calculations of the bandstructure with carrier generation and transport model of this detector, we study the essential physics of this novel detector design and project its performance. Combining the best of both these materials can yield high performance and superior detection capabilities.

Solitonic magnetic excitations such as domain walls and, specifically, skyrmionics enable the possibility of compact, high density, ultrafast, all-electronic, low-energy devices, which is the basis for the emerging area of skyrmionics. The topological winding of skyrmion spins affects their overall lifetime, energetics, and dynamical behavior. In this Perspective, we discuss skyrmionics in the context of the present-day solid-state memory landscape and show how their size, stability, and mobility can be controlled by material engineering, as well as how they can be nucleated and detected. Ferrimagnets near their compensation points are promising candidates for this application, leading to a detailed exploration of amorphous CoGd as well as the study of emergent materials such as Mn4N and inverse Heusler alloys. Along with material properties, geometrical parameters such as film thickness, defect density, and notches can be used to tune skyrmion properties, such as their size and stability. Topology, however, can be a double-edged sword, especially for isolated metastable skyrmions, as it brings stability at the cost of additional damping and deflective Magnus forces compared to domain walls. Skyrmion deformation in response to forces also makes them intrinsically slower than domain walls. We explore potential analog applications of skyrmions, including temporal memory at low density—one skyrmion per racetrack—that capitalizes on their near ballistic current–velocity relation to map temporal data to spatial data and decorrelators for stochastic computing at a higher density that capitalizes on their interactions. We summarize the main challenges of achieving a skyrmionics technology, including maintaining positional stability with very high accuracy and electrical readout, especially for small ferrimagnetic skyrmions, deterministic nucleation, and annihilation and overall integration with digital circuits with the associated circuit overhead.

Ultraviolet (UV) and infrared (IR) detector and array technologies have proven to be at the heart of many remote sensing instruments for various NASA missions. These exciting AlGaN ultraviolet avalanche photodiode (UV-APD) and HgCdTe-graphene photodetector and focal plane array (FPA) technologies are being developed for high performance UV and IR sensing to support and further advance a variety of NASA Earth Science applications. This paper will present our recent results on GaN/AlGaN UV-APDs grown by metal-organic chemical vapor deposition (MOCVD) on GaN substrates with avalanche gains greater than 5×106, and high responsivities. We are also developing room temperature operating graphene-enhanced HgCdTe mid-wave infrared (MWIR) detectors and focal plane arrays (FPAs). These compact and low-cost MWIR sensors can benefit various NASA remote sensing applications. For MWIR detection it is very desirable to develop IR detector technologies that operate at or near room temperature to minimize cooling requirements. The 2-5 μm MWIR spectral band is useful for measuring sea surface temperatures, cloud properties, volcanic activities, and forest fires, among other applications. Using low size, weight, power, and cost MWIR sensors on smaller platforms in low orbit can enable improved measurements of thermal dynamics with high spatial resolution. We will discuss modelling and experimental results for these devices.

The end of Moore’s Law and the rise of “smart” consumer electronics has wide opened the gate for creative hardware design for the next few decades. While linear algebra accelerators and emulated hardware on FPGA has made some advances in this direction, a fundamentally different approach is required for reaching the efficiency and performance that will be necessary to embed cognitive computing in-situ in these next generation devices. To address this problem, in this work, we present a collection of spintronic hardware building blocks, fabricable with present day technology, that can be used to build biologically inspired neuromorphic hardware. These hardware units provide neuromorphic behavior derived from their physics and manifested in their electrical characteristics, therefore opening the pathway for compact, low power and VLSI grade scalability using these units. The collection contains two types of stochastic neuron (SN) devices: Analog (ASN) and Binary (BSN) as well as multi-level programmable synaptic connections that can be used for implementing compact dendrites. We discuss the area and power savings brought on by these building blocks and compared with an example design using FPGAs. This functionally complete but minimal set of neuromorphic building blocks can be used to implement a variety of neuromorphic architectures, as demonstrated in this work. We end the discussion with design ideas for neuromorphic architectures, which do not merely implement fast linear algebra but go beyond to elevate compact, physics-based field programmable neuromorphic arrays as first class citizens in every designers toolkit.

The infrared sensing market has experienced increased attention in recent years, due in large part to a widening of the application space from defense and research to commercial and consumer avenues. The uncooled market has been dominated by microbolometers, yet a recent resurgence of polycrystalline PbSe photoconductors could be the key to infrared photon detectors operating at room temperature. Typically, PbSe detectors are operated at 230 K, and can achieve sensitivity similar to its cryogenically cooled counterparts. In an effort to develop truly uncooled photon detectors, we have investigated surface plasmon resonant (SPR) structures for MWIR sensitivity enhancement of PbSe photoconductors. Au disc-shaped nanostructures were modeled to determine the required dimensions for a targeted resonance region between 3.5 μm to 4 μm. Finite element modeling (FEM) was then used to determine the effect of square disc arrays on the absorption of PbSe thin films. Modeled results suggest up to a three-fold increase in PbSe absorption in films with embedded structures. Square disc arrays made up of 500 nm and 1000 nm discs were patterned, fabricated, and embedded into sensitized polycrystalline PbSe thin films. FTIR spectra were collected to validate the model and determine the viability of SPR structures in a polycrystalline thin films. Based on FEM results, numerical models were constructed to predict photoconductor performance. Herein, we present the design, modeling, fabrication, and measured spectra of Au disc arrays embedded in PbSe films, as well as MWIR photoconductor performance predictions suggesting increased operating temperature with the utilization of SPR structures.

A high-performance graphene-based HgCdTe detector technology is being developed for sensing over the mid-wave infrared (MWIR) band for NASA Earth Science, defense, and commercial applications. This technology involves the integration of graphene into HgCdTe photodetectors that combines the best of both materials and allows for higher MWIR(2-5 m) detection performance compared to photodetectors using only HgCdTe material. The interfacial barrier between the HgCdTe-based absorber and the graphene layer reduces recombination of photogenerated carriers in the detector. The graphene layer also acts as high mobility channel that whisks away carriers before they recombine, further enhancing the detector performance. Likewise, HgCdTe has shown promise for the development of MWIR detectors with improvements in carrier mobility and lifetime. The room temperature operational capability of HgCdTe-based detectors and arrays can help minimize size, weight, power and cost for MWIR sensing applications such as remote sensing and earth observation, e.g., in smaller satellite platforms. The objective of this work is to demonstrate graphene-based HgCdTe room temperature MWIR detectors and arrays through modeling, material development, and device optimization. The primary driver for this technology development is the enablement of a scalable, low cost, low power, and small footprint infrared technology component that offers high performance, while opening doors for new earth observation measurement capabilities.

Clean images are an important requirement for machine vision systems to recognize visual features correctly. However, the environment, optics, electronics of the physical imaging systems can introduce extreme distortions and noise in the acquired images. In this work, we explore the use of reservoir computing, a dynamical neural network model inspired from biological systems, in creating dynamic image filtering systems that extracts signal from noise using inverse modeling. We discuss the possibility of implementing these networks in hardware close to the sensors., 5 pages, 4 figures, To appear in Conference Proceedings of The 44th Annual Conference of IEEE Industrial Electronics Society (2018): Special Session on Machine Vision, Control and Navigation

Polycrystalline Pb-Salt detectors are a low cost technology for IR photodetectors. In this work, we describe the physics of PbSe detectors that accurately captures the carrier transport and long lifetimes arising from inverted channels formed due to unique bandstructure of PbSe|PbI 2 heterostructures. We then discuss performance improvement through voltage control of the bandstructure, which may open a pathway for high performance and low cost IR photodetectors.

In this work we show how we can build a technology platform for cognitive imaging sensors using recent advances in recurrent neural network architectures and training methods inspired from biology. We demonstrate learning and processing tasks specific to imaging sensors, including enhancement of sensitivity and signal-to-noise ratio (SNR) purely through neural filtering beyond the fundamental limits sensor materials, and inferencing and spatio-temporal pattern recognition capabilities of these networks with applications in object detection, motion tracking and prediction. We then show designs of unit hardware cells built using complementary metal-oxide semiconductor (CMOS) and emerging materials technologies for ultra-compact and energy-efficient embedded neural processors for smart cameras., 11 pages, 5 figures. To be presented in SPIE DCS 2018: Image Sensing Technologies: Materials, Devices, Systems, and Applications V

Over the past decade a large family of spintronic devices have been proposed as candidates for replacing CMOS for future digital logic circuits. Using the recently developed Modular Approach framework, we investigate and identify the physical bottlenecks and engineering challenges facing current spintronic devices. We then evaluate how systematic advancements in material properties and device design innovations impact the performance of spintronic devices, as a possible continuation of Moore's Law, even though some of these projections are speculative and may require technological breakthroughs. Lastly, we illustrate the use of the Modular Approach as an exploratory tool for probabilistic networks, using superparamagnetic magnets as building blocks for such networks. These building blocks leverage the inherent physics of stochastic spin-torque switching and could provide ultra-compact and efficient hardware for beyond-Boolean computational paradigms., Comment: 20 pages, 7 figures. Includes supplementary at the end

STT-RAMs show the promise to be the universal memory device with applications in embedded devices. There are outstanding challenges that need to be addressed before a wide-scale adoption of this technology happens. The solution to these challenges lie in integration of emerging high performance spintronic materials as well as clever circuit based techniques to operate these devices at their peak performance. In this work we present a material-device-circuit co-design framework that connects the properties of materials and transport physics to circuits and systems performance. To illustrate the use of this framework we study present and next generation STT-RAM technology in terms of energy-delay-reliability performance metrics and suggest possible directions for future generation devices.