arXiv subjects

Samiran Ganguly

Publications and source records attributed to Samiran Ganguly.

At least 19 recordsLinked to original sources

arXiv2025

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↗

arXiv2024

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↗

arXiv2024

Connecting physics to systems with modular spin-circuits

An emerging paradigm in modern electronics is that of CMOS + $\sf X$ requiring the integration of standard CMOS technology with novel materials and technologies denoted by $\sf X$. In this context, a crucial challenge is to develop accurate circuit models for $\sf X$ that are compatible with standard models for CMOS-based circuits and systems. In this perspective, we present physics-based, experimentally benchmarked modular circuit models that can be used to evaluate a class of CMOS + $\sf X$ systems, where $\sf X$ denotes magnetic and spintronic materials and phenomena. This class of materials is particularly challenging because they go beyond conventional charge-based phenomena and involve the spin degree of freedom which involves non-trivial quantum effects. Starting from density matrices $-$ the central quantity in quantum transport $-$ using well-defined approximations, it is possible to obtain spin-circuits that generalize ordinary circuit theory to 4-component currents and voltages (1 for charge and 3 for spin). With step-by-step examples that progressively become more complex, we illustrate how the spin-circuit approach can be used to start from the physics of magnetism and spintronics to enable accurate system-level evaluations. We believe the core approach can be extended to include other quantum degrees of freedom like valley and pseudospins starting from corresponding density matrices.

cond-mat.mes-hall↗

arXiv2024

Reconfigurable Stochastic Neurons Based on Strain Engineered Low Barrier Nanomagnets

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. "Binary" stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and -1, with the probability of being in either level determined by an external bias. "Analog" stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence "analog") and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer, and the energy cost for this type of reconfiguration is miniscule.

cs.ET↗

arXiv2023

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↗

arXiv2023

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↗

arXiv2023

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↗

arXiv2023

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↗

arXiv2022

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↗

arXiv2021

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↗

arXiv2021

Computing and Memory Technologies based on Magnetic Skyrmions

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 review, 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. Ferrimagnetsnear their compensation points are important candidates for this application, leading to detailed exploration of amorphous CoGd as well as the study of emergent materials such as Mn$_4$N 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, and decorrelator for stochastic computing at a higher density that capitalizes on their interactions. We summarize the main challenges to achieve a skyrmionics technology, including maintaining positional stability with very high accuracy, electrical readout, especially for small ferrimagnetic skyrmions, deterministic nucleation and annihilation, and overall integration with digital circuits with the associated circuit overhead.

cond-mat.mtrl-sci↗

arXiv2021

A Physics Based Multiscale Compact Model of p-i-n Avalanche Photodiodes

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.

physics.ins-det↗

arXiv2020

Extrinsic Voltage Control of Carrier Lifetime in Polycrystalline PbSe Mid-wave IR Photo Detectors for Increased Detectivity

Polycrystalline PbSe for mid-wave IR (MWIR) photodetector is an attractive material option due to high operating/ambient temperature operation and relatively easy and cheap fabrication process, making it candidate for low-power and small footprint applications such as internet-of-thing (IoT) sensors and deployment on mobile platforms due to reduced/removed active cooling requirements. However, there are many material challenges that reduce the detectivity of these detectors. In this work, we demonstrate that it is possible to improve upon this metric by externally modulating the lifetime of conducting carriers by application of a back-gate voltage that can control the recombination rate of generated carrier. We first describe the physics of $PbSe$ detectors, the mechanisms underlying carrier transport, and long observed lifetimes of conducting carriers. We then discuss the voltage control of these inverted channels using a back-plane gate resulting in modulation of the lifetime of these carriers. This voltage control represents and extrinsic "knob" through which it may be possible to open a pathway for design of high performance IR photodetectors, as shown in this work.

physics.app-ph↗

arXiv2020

Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics

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.

cs.ET↗

arXiv2020

Temporal Memory with Magnetic Racetracks

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.

physics.app-ph↗

arXiv2019

Non-Equilibrium Green's Function based Circuit Models for Coherent Spin Devices

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 $4\times4$ matrices. Starting from the Non-Equilibrium Green's Function (NEGF) formalism in the elastic, phase-coherent transport regime, we develop spin generalized Landauer-Büttiker formulas involving such $4\times 4$ conductances, for multi-terminal devices in the presence of Normal-Metal (NM) 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 (FM) 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 2D. Our results allows 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.

cond-mat.mes-hall↗

arXiv2018

Analog Signal Processing Using Stochastic Magnets

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.

cs.ET↗

arXiv2018

Reservoir Computing based Neural Image Filters

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.

cs.CV↗