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Avik W. Ghosh

Publications and source records attributed to Avik W. Ghosh.

At least 19 recordsLinked to original sources

Enhanced detection of circularly polarized photons with topological materials

Topological insulators (TI) are highly attractive platforms for next-generation optoelectronic and photonic devices. Spin-momentum locking of topological surface states enhances their nonlinear optical responses and sensitivities, especially to circularly polarized light. Until now, theoretical investigations of nonlinear responses in TIs have been limited to microscopic calculations on analytical continuum models, or leveraging density functional theory based Hamiltonians. In this work, we expand beyond these two approaches by employing a nonlinear Kubo formalism to calculate second-order nonlinear optical conductivity in a slab geometry using symmetry informed tight binding models that accurately reproduce the conduction, valence and topological surface bands in Bi$_2$Se$_3$. Our methodology enables us to study the layer resolved contribution to injection currents coupled to the incident electric field. We demonstrate that our technique can reveal how device engineering modifies elements of the nonlinear optical response such as the circular {and linear} photogalvanic effects by breaking inversion and time-reversal symmetry. {In particular, magnetization-induced symmetry breaking enables nonlinear conductivity tensor components (e.g., $σ_{xyz}$) that are normally forbidden by symmetry, thereby directly modifying the circular photogalvanic effect.} We find, in line with experiments, that the photogalvanic current is sensitive to field effects, Fermi level energy, gate voltage and the energy of incident light. Our computed midwave infrared (mid-IR) responsivity $R \approx 0.170~\mathrm{μA/W}$ is comparable to reported TI and intrinsic 2D-material photodetectors. We simulate experimentally unexplored methods to modify the circular photogalvanic effect such as proximitizing a magnetic field to one of the TI surface materials, suggesting a mechanism for optoelectronic tuning.

cond-mat.other

Switching perpendicular magnets for Processing-in-memory with voltage gated Weyl Semimetals

Processing-in-memory (PIM) reduces data transfer latency by rolling memory and logic elements into one compute location. As an emergent material candidate for such an architecture, we propose a strained Weyl semimetal based spin-orbit-torque random-access memory (SWSM-SOTRAM) device. The spin-orbit torque (SOT) originates from two mechanisms: (1) the inverse spin Galvanic effect (iSGE), which generates nonequilibrium in-plane spin accumulation at interfaces, and (2) a bulk spin Hall effect (SHE), which produces a transverse spin current carrying out-of-plane spin angular momentum. The latter is tunable via an exchange Zeeman field. Both effects are evaluated using the tight-binding model coupled with a nonequilibrium Green's function (TB-NEGF) formalism for quantum transport. Information write is achieved through SOT switching of an out-of-plane free magnet. A piezo attached to a magnetostrictive selector modulates the strain in the latter, leading to the rotation of the magnetization and hence the exchange Zeeman field exerted on the Weyl semimetal. This strain-controlled exchange field enables the symmetry tuning of the Weyl semimetal and modulation of its spin Hall effect. The TB-NEGF calculations of SHE and iSGE, combined with Landau-Lifshitz-Gilbert (LLG) simulations of magnetization dynamics, establish the SOT switching mechanism and demonstrate a pathway toward the SWSM-SOTRAM PIM device.

cond-mat.mes-hall

A Matrix Quantum Kinetic Treatment of Impact Ionization in Avalanche Photodiodes

Matrix based quantum kinetic simulations have been widely used for the predictive modeling of electronic devices. Inelastic scattering from phonons and electrons are typically treated as higher order processes in these treatments, captured using mean-field approximations. Carrier multiplication in Avalanche Photodiodes (APDs), however, relies entirely on strongly inelastic impact ionization, making electron-electron scattering the dominant term requiring a rigorous, microscopic treatment. We go well beyond the conventional Born approximation for scattering to develop a matrix-based quantum kinetic theory for impact ionization, involving products of multiple Green's functions. Using a model semiconductor in a reverse-biased p-i-n configuration, we show how its calculated non-equilibrium charge distributions show multiplication at dead-space values consistent with energy-momentum conservation. Our matrix approach can be readily generalized to more sophisticated atomistic Hamiltonians, setting the stage for a fully predictive, `first principles' theory of APDs.

cond-mat.mes-hall

Adaptive Synaptogenesis Implemented on a Nanomagnetic Platform

The human brain functions very differently from artificial neural networks (ANN) and possesses unique features that are absent in ANN. An important one among them is "adaptive synaptogenesis" that modifies synaptic weights when needed to avoid catastrophic forgetting and promote lifelong learning. The key aspect of this algorithm is supervised Hebbian learning, where weight modifications in the neocortex driven by temporal coincidence are further accepted or vetoed by an added control mechanism from the hippocampus during the training cycle, to make distant synaptic connections highly sparse and strategic. In this work, we discuss various algorithmic aspects of adaptive synaptogenesis tailored to edge computing, demonstrate its function using simulations, and design nanomagnetic hardware accelerators for specific functions of synaptogenesis.

cond-mat.dis-nn

Strained topological insulator spin-orbit torque random access memory (STI-SOTRAM) bit cell for energy-efficient Processing in Memory

We present a novel design of a strained topological insulator spin-orbit torque random access memory (STI-SOTRAM) bit cell comprising a piezoelectric/magnet (gating)/topological insulator (TI)/magnet (storage) heterostructure that leverages the TI's high charge-to-spin conversion efficiency coupled with the piezo-induced strain-based gating mechanism for low-power in-memory computing. The piezo-induced strain effectively modulates the conductivity of the topological surface state (TSS) by altering the gating magnet's magnetization from out-to-in-plane, facilitating the storage magnet's spin-orbit torque (SOT) switching. Through comprehensive coupled stochastic Landau-Lifshitz-Gilbert (LLG) simulations, we explore the device dynamics, anisotropy-stress phase space for switching, and write conditions and demonstrate a significant reduction in energy dissipation compared to conventional heavy metal (HM)-based SOT switching. Additionally, we project the energy consumption for in-memory Boolean operations (AND and OR). Our findings suggest the promise of the STI-SOTRAM for low-power, high-performance edge computing.

cond-mat.mes-hall

Roadmap for Unconventional Computing with Nanotechnology

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

cs.ET

Temperature-Resilient True Random Number Generation with Stochastic Actuated Magnetic Tunnel Junction Devices

Nanoscale magnetic tunnel junction (MTJ) devices can efficiently convert thermal energy in the environment into random bitstreams for computational modeling and cryptography. We recently showed that perpendicular MTJs activated by nanosecond pulses can generate true random numbers at high data rates. Here, we explore the dependence of probability bias-the deviations from equal probability (50/50) 0/1 bit outcomes-of such devices on temperature, pulse amplitude, and duration. Our experimental results and device model demonstrate that operation with nanosecond pulses in the ballistic limit minimizes variation of probability bias with temperature to be far lower than that of devices operated with longer-duration pulses. Further, operation in the short-pulse limit reduces the bias variation with pulse amplitude while rendering the device more sensitive to pulse duration. These results are significant for designing TRNG MTJ circuits and establishing operating conditions.

cond-mat.mes-hall

Reduced sensitivity to process, voltage and temperature variations in activated perpendicular magnetic tunnel junctions based stochastic devices

True random number generators (TRNGs) are fundamental building blocks for many applications, such as cryptography, Monte Carlo simulations, neuromorphic computing, and probabilistic computing. While perpendicular magnetic tunnel junctions (pMTJs) based on low-barrier magnets (LBMs) are natural sources of TRNGs, they tend to suffer from device-to-device variability, low speed, and temperature sensitivity. Instead, medium-barrier magnets (MBMs) operated with nanosecond pulses - denoted, stochastic magnetic actuated random transducer (SMART) devices - are potentially superior candidates for such applications. We present a systematic analysis of spin-torque-driven switching of MBM-based pMTJs (Eb ~ 20 - 40 kBT) as a function of pulse duration (1 ps to 1 ms), by numerically solving their macrospin dynamics using a 1-D Fokker-Planck equation. We investigate the impact of voltage, temperature, and process variations (MTJ dimensions and material parameters) on the switching probability of the device. Our findings indicate SMART devices activated by short-duration pulses (< 1 ns) are much less sensitive to process-voltage-temperature (PVT) variations while consuming lower energy (~ fJ) than the same devices operated with longer pulses. Our results show a path toward building fast, energy-efficient, and robust TRNG hardware units for solving optimization problems.

physics.app-ph

Phase Change Induced Magnetic Switching through Metal-insulator Transition in VO2/TbFeCo Films

The ability to manipulate spins in magnetic materials is essential in designing spintronics devices. One method for magnetic switching is through strain. In VO2 on TiO2 thin films, while VO2 remains rutile across the metal-insulator transition, the in-plane lattice area expands going from low temperature insulating phase to high temperature conducting phase. In a VO2/TbFeCo bilayer, the expansion of the VO2 lattice area exerts tension on the amorphous TbFeCo layer. Through the strain effect, magnetic properties, including the magnetic anisotropy and magnetization, of TbFeCo can be changed. In this work, the changes in magnetic properties of TbFeCo on VO2/TiO2(011) are demonstrated using anomalous Hall effect measurements. Across the metal-insulator transition, TbFeCo loses perpendicular magnetic anisotropy, and the magnetization in TbFeCo turns from out-of-plane to in-plane. Using atomistic simulations, we confirm these tunable magnetic properties originating from the metal-insulator transition of VO2. This study provides the groundwork for controlling magnetic properties through a phase transition.

cond-mat.mtrl-sci

Choose your tools carefully: A Comparative Evaluation of Deterministic vs. Stochastic and Binary vs. Analog Neuron models for Implementing Emerging Computing Paradigms

Neuromorphic computing, commonly understood as a computing approach built upon neurons, synapses, and their dynamics, as opposed to Boolean gates, is gaining large mindshare due to its direct application in solving current and future computing technological problems, such as smart sensing, smart devices, self-hosted and self-contained devices, artificial intelligence (AI) applications, etc. In a largely software-defined implementation of neuromorphic computing, it is possible to throw enormous computational power or optimize models and networks depending on the specific nature of the computational tasks. However, a hardware-based approach needs the identification of well-suited neuronal and synaptic models to obtain high functional and energy efficiency, which is a prime concern in size, weight, and power (SWaP) constrained environments. In this work, we perform a study on the characteristics of hardware neuron models (namely, inference errors, generalizability and robustness, practical implementability, and memory capacity) that have been proposed and demonstrated using a plethora of emerging nano-materials technology-based physical devices, to quantify the performance of such neurons on certain classes of problems that are of great importance in real-time signal processing like tasks in the context of reservoir computing. We find that the answer on which neuron to use for what applications depends on the particulars of the application requirements and constraints themselves, i.e., we need not only a hammer but all sorts of tools in our tool chest for high efficiency and quality neuromorphic computing.

cs.ET

A True Random Number Generator for Probabilistic Computing using Stochastic Magnetic Actuated Random Transducer Devices

Magnetic tunnel junctions (MTJs), which are the fundamental building blocks of spintronic devices, have been used to build true random number generators (TRNGs) with different trade-offs between throughput, power, and area requirements. MTJs with high-barrier magnets (HBMs) have been used to generate random bitstreams with $\lesssim$ 200~Mb/s throughput and pJ/bit energy consumption. A high temperature sensitivity, however, adversely affects their performance as a TRNG. Superparamagnetic MTJs employing low-barrier magnets (LBMs) have also been used for TRNG operation. Although LBM-based MTJs can operate at low energy, they suffer from slow dynamics, sensitivity to process variations, and low fabrication yield. In this paper, we model a TRNG based on medium-barrier magnets (MBMs) with perpendicular magnetic anisotropy. The proposed MBM-based TRNG is driven with short voltage pulses to induce ballistic, yet stochastic, magnetization switching. We show that the proposed TRNG can operate at frequencies of about 500~MHz while consuming less than 100~fJ/bit of energy. In the short-pulse ballistic limit, the switching probability of our device shows robustness to variations in temperature and material parameters relative to LBMs and HBMs. Our results suggest that MBM-based MTJs are suitable candidates for building fast and energy-efficient TRNG hardware units for probabilistic computing.

physics.app-ph

A simplified drift-diffusion model for pandemic propagation

Predicting Pandemic evolution involves complex modeling challenges, often requiring detailed discrete mathematics executed on large volumes of epidemiological data. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. We further simplify one of those equations, the SIR model, by offering quasi-analytical solutions and fitting functions that agree well with the numerics, as well as COVID-19 data across a few countries. The equations provide an elegant way to visualize the evolution, by mapping onto the dynamics of an overdamped classical particle moving in the SIR configuration space, drifting down gradient of a potential whose shape is set by the model and parameters in hand. We discuss potential sources of errors in our analysis and their growth over time, and map those uncertainties into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model could be particularly useful in making policy decisions going forward.

physics.bio-ph

A Deep Dive into the Computational Fidelity of High Variability Low Energy Barrier Magnet Technology for Accelerating Optimization and Bayesian Problems

Low energy barrier magnet (LBM) technology has recently been proposed as a candidate for accelerating algorithms based on energy minimization and probabilistic graphs because their physical characteristics have a one-to-one mapping onto the primitives of these algorithms. Many of these algorithms have a much higher tolerance for error compared to high-accuracy numerical computation. LBM, however, is a nascent technology, and devices show high sample-to-sample variability. In this work, we take a deep dive into the overall fidelity afforded by this technology in providing computational primitives for these algorithms. We show that while the compute results show finite deviations from zero variability devices, the margin of error is almost always certifiable to a certain percentage. This suggests that LBM technology could be a viable candidate as an accelerator for popular emerging paradigms of computing.

cs.ET

Low power In Memory Computation with Reciprocal Ferromagnet/Topological Insulator Heterostructures

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.

cond-mat.mes-hall

Direct evidence of Klein-antiKlein tunneling of graphitic electrons in a Corbino geometry

Transport measurement of electron optics in monolayer graphene p-n junction devices has been traditionally studied with negative refraction and chiral transmission experiments in Hallbar magnetic focusing set-ups. We show direct signatures of Klein (monolayer) and anti-Klein (bilayer) tunneling with a circular 'edgeless' Corbino geometry made out of gated graphene p-n junctions. Noticeable in particular is the appearance of angular sweet spots (Brewster angles) in the magnetoconductance data of bilayer graphene, which minimizes head-on transmission, contrary to conventional Fresnel optics or monolayer graphene which shows instead a sharpened collimation of transmission paths. The local maxima on the bilayer magnetoconductance plots migrate to higher fields with increasing doping density. These experimental results are in good agreement with detailed numerical simulations and analytical predictions.

cond-mat.mes-hall

Dynamical system-based computational models for solving combinatorial optimization on hypergraphs

The intrinsic energy minimization in dynamical systems offers a valuable tool for minimizing the objective functions of computationally challenging problems in combinatorial optimization. However, most prior works have focused on mapping such dynamics to combinatorial optimization problems whose objective functions have quadratic degree (e.g., MaxCut); such problems can be represented and analyzed using graphs. However, the work on developing such models for problems that need objective functions with degree greater than two, and subsequently, entail the use of hypergraph data structures, is relatively sparse. In this work, we develop dynamical system-inspired computational models for several such problems. Specifically, we define the 'energy function' for hypergraph-based combinatorial problems ranging from Boolean SAT and its variants to integer factorization, and subsequently, define the resulting system dynamics. We also show that the design approach is applicable to optimization problems with quadratic degree, and use it develop a new dynamical system formulation for minimizing the Ising Hamiltonian. Our work not only expands on the scope of problems that can be directly mapped to, and solved using physics-inspired models, but also creates new opportunities to design high-performance accelerators for solving combinatorial optimization.

physics.app-ph

Positional stability of skyrmions in a racetrack memory with notched geometry

Magnetic skyrmions are chiral spin textures with attractive features, such as ultra-small size, solitonic nature, and easy mobility with small electrical currents that make them promising as information-carrying bits in low-power high-density memory, and logic applications. However, it is essential to guarantee the positional stability of skyrmions for reliable information extraction. Using micromagnetic simulations for the minimum energy path (MEP), we compute the energy barriers associated with stabilizing notches along a racetrack. We vary material parameters, specifically, the strength of the chiral Dzyaloshinskii-Moriya interactions (DMI), the notch geometry, and the thickness of the racetrack to get the optimal barrier height. We find that the reduction of skyrmion size as it squeezes past the notch gives rise to the energy barrier. We find a range of energy barriers up to ~ 45 kBT for a racetrack of 5 nm thickness that can provide years long positional lifetime of skyrmions for long-term memory applications while requiring a moderate amount of current (~ 10^10 A/m2) to move the skyrmions. Furthermore, we derive quasi-analytical equations to estimate the energy barrier. We also explore other pinning mechanisms, such as a local variation of material parameters in a region, and find that notched geometry provides the highest energy barrier. Our results open up possibilities to design practical skyrmion-based racetrack geometries for spintronics applications.

cond-mat.mes-hall

Anatomy of nanomagnetic switching at a 3D Topological Insulator PN junction

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 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 the surface current density, which in turn is limited by bulk band gap. {Using quantum kinetic models, we calculate the spatially varying spin potential and quantify the localization of the current vs applied bias}. Additionally, with the magnetodynamic simulation of a soft nanomagnet, 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.

cond-mat.mes-hall