Photon detectors are increasingly utilizing quantum features to enhance their performance. Cutting down on dark current by reducing absorber thickness necessitates electron/hole carrier transport engineering to obtain gain via quantum features. Superlattices, barrier-based detectors, and quantum materials such as Floquet engineered systems are poorly served by semi-classical transport modeling approaches and a fully quantum approach is warranted to capture all quantum features from a bottom-up fashion. These effects include non-parabolic and low-dimensional bandstructures, tunneling, resonant transport, dynamic (Floquet) and low-dimensional (Anderson) localization, transport mediation via phonons, plasmons, and photons, as well as various recombination and carrier generation/multiplication methods. In this work we present a systematic framework for quantum transport modeling of detectors via the Non-Equilibrium Green’s Function (NEGF) formalism. This formalism is highly modular in terms of extending the transport related physics, as well robust in handling arbitrary material stack, given a Hamiltonian description. This method has been successfully used in analysis of highly scaled, 2D and nanowire transistor devices, transport in novel quantum materials, and in non-equilibrium thermal transport, and therefore forms a solid foundation for building our platform. However, many open challenges remain in doing so, and in this work, we describe our recent efforts towards advancing this framework for its adoption as a preferred tool for next generation quantum enhanced photon-detectors.
Quantum systems are entering a crucial stage of technology development where it is critical that design automation tools are co-developed along with the technology itself. Electronics design ecosystem provides tools which may be extended towards simulation and verification, critical steps towards large-scale certifiable designs of such quantum systems. SPICE simulations provide an appropriate level of abstraction that is both physical, in terms of custom designed physical components, and simulation approach, i.e. differential-algebraic equations. In this work we describe our efforts in modeling quantum + classical systems within SPICE. We first present our highly successful spintronics platform that has allowed us to model a multitude of spintronic effects including transport in tunnel-junctions, full lifecycle of quasi-quantum topological objects such as skyrmions, and transport in topological materials (TI, Weyl Semimetals). We demonstrate the extension of this platform towards simulations of circuits built using spin-qubits made from Josephson junctions, and a more emergent platform of Majorana Zero Modes (MZMs). We describe our approach that allows us to abstract away microscopic details, while capturing device and circuit behavior using controlled sources and custom components. We describe our approach to embed full dynamic solutions of alternate non-electrical state variables and indeed abstract quantities within the framework of SPICE. Our approach interplays well with more “fundamental” modeling approaches such as quantum master equations and non-equilibrium Green’s functions, as well as more “system” level modeling approaches such as SystemVerilog, thereby bridging both these worlds for exploration, analysis, simulation, and verification of scaled quantum systems.
A scalable, low cost, low power, and small footprint uncooled mid-wave infrared (MWIR) sensing technology capable of measuring thermal dynamics with high spatial resolution can be of great benefit to space and satellite applications such as remote sensing and earth observation. Conventional photodetectors designed to absorb MWIR band wavelengths have often been based on HgCdTe material and typically require cooling. However, through integration of bilayer graphene functioning as a high mobility channel with HgCdTe material in photodetectors, higher performance detection over the 2-5 μm MWIR band may be enabled and facilitated primarily by thus limiting recombination of photogenerated carriers in these detectors. This high performance MWIR band detector technology is being developed and tested for NASA Earth Science, defense, and commercial applications. Graphene bilayers on Si/SiO2 substrates are doped with boron using a spin-on dopant (SOD) process and then transferred onto HgCdTe substrates for enhanced mobility photodetection applications. Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and secondary-ion mass spectroscopy (SIMS) were utilized for analysis of dopant levels and structural properties of the graphene throughout various stages of the development process to characterize the p-doped graphene following doping and transfer. The enhanced performance and functional capabilities of the room-temperature operating graphene-based HgCdTe MWIR detectors and arrays are thereby demonstrated through modeling, material development and characterization, and device optimization.
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.
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 the integration of graphene with HgCdTe photodetectors that combines the best of both materials, and allows for higher MWIR (2-5 μm) detection performance compared with photodetectors using only HgCdTe material. The interfacial barriers between the HgCdTe-based absorber and the graphene act as a tunable rectifier that reduces the 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 detection performance. This makes them much more practical and useful for MWIR sensing applications such as remote sensing and earth observation, e.g., in smaller satellite platforms (CubeSat) for measurement of thermal dynamics with better spatial resolution. 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.
Many III-V digital alloy avalanche photodiodes have experimentally demonstrated very low excess noise. The presence of minigaps and enhanced valence band effective mass leads to the enhanced performance. Using first principle calculations and environment-dependent tight binding model we study the correlation of these properties with material parameters like stress. Furthermore, using NEGF formalism we study how these minigaps and mass enhancement impact the electron tunneling and phonon scattering processes in digital alloys. Based on our calculations, we propose some empirical inequalities for quantifying the effectiveness of such minigaps in making the device unipolar and thus high gain.
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 the integration of graphene with HgCdTe photodetectors that combines the best of both materials, and allows for higher MWIR (2-5 μm) detection performance compared with photodetectors using only HgCdTe material. The interfacial barriers between the HgCdTe-based absorber and the graphene act as a tunable rectifier that reduces the 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 detection performance. This makes them much more practical and useful for MWIR sensing applications such as remote sensing and earth observation, e.g., in smaller satellite platforms (CubeSat) for measurement of thermal dynamics with better spatial resolution. 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.
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 the integration of graphene with HgCdTe photodetectors that combines the best of both materials, and allows for higher MWIR (2-5 μm) detection performance compared with photodetectors using only HgCdTe material. The interfacial barriers between the HgCdTe-based absorber and the graphene act as a tunable rectifier that reduces the 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 detection performance. This makes them much more practical and useful for MWIR sensing applications such as remote sensing and earth observation, e.g., in smaller satellite platforms (CubeSat) for measurement of thermal dynamics with better spatial resolution. 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.
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.
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.
KEYWORDS: Systems modeling, Avalanche photodetectors, Avalanche photodiodes, Telecommunications, Monte Carlo methods, Instrument modeling, Internet, Photonic devices, Electronic components, Sensing systems
Some III-V digital alloy avalanche photodiodes demonstrate very low excess noise making them suitable for single photon detection applications. This behavior is attributed to the presence of minigaps in the valence band and high hole effective mass which reduce hole impact ionization. In this work, we present a physics based SPICE compatible compact model for these low noise avalanche photodiodes built from parameters extracted from Environment-Dependent Tight Binding model, that is calibrated to ab-initio Density Functional Theory, and Monte Carlo methods. Using this approach, we can accurately capture the physical characteristics of APDs in integrated photonics circuit simulation.
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.
With increasing demand for low-power embedded electronics for Internet of Things (IoT) applications, nanomagnetic devices have emerged as strong potential candidates to complement present day electronics. A variety of novel switching effects such as Spin Transfer Torque (STT), Spin Orbit Torque (SOT) and Giant Spin Hall Effect (GSHE) offer complementary ways to manipulate nano-sized magnets. However, the low intrinsic energy cost of switching spins is often compromised by the energy consumed in the overhead circuitry in creating the necessary switching fields. In fact, the two major challenges are size scalability – limited by the superparamagnetic limit, and the energy-delay-reliability cost, limited by spin stagnation in STT devices and defect pinning in SOT driven domain wall devices. We discuss two possible alternatives that attempt to bypass these limits. Skyrmions stabilized in thin magnetic films can bypass the super-paramagnetic limit and carry information bits with added protection accorded by size and topological barrier. We present candidate ferrimagnetic materials with low saturation magnetization and damping needed to enable ultrafast room temperature soliton like skyrmion propagation. A different solution at a systems level is to design interconnected low-barrier soft magnets with controlled stochastic dynamics that can be used to realize analog or digital hardware neurons. We show how such analog neurons can directly implement Reservoir Computing for temporal inferencing, tracking and real time pattern recognition.
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 μm) 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.
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.
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