arXiv subjects

Pedram Khalili Amiri

Publications and source records attributed to Pedram Khalili Amiri.

At least 19 recordsLinked to original sources

arXiv2026

A Voltage-controlled MTJ-CMOS Neuron Emulating Tunable Izhikevich-Inspired Dynamics

Biological neurons exhibit diverse firing dynamics that enable adaptive and stimulus-dependent signalling, yet reproducing these dynamics in hardware has remained an enduring challenge. In this work, we present an Izhikevich- inspired reconfigurable neuron that co-designs voltage-controlled magnetic tunnel junction (V-MTJ) dynamics with CMOS circuitry. The proposed architecture combines V-MTJ excitability dynamics, enabled by a tunable energy landscape, with CMOS recovery dynamics to generate five distinct neuronal firing pat- terns with different spiking, bursting and response characteristics. Our results, based on measured V-MTJ characteristics and circuit simulations using com- mercial GlobalFoundries 22-nm FD-SOI CMOS technology, show an average energy consumption of 145.44 fJ per spike. Algorithmic simulations further show that these firing dynamics reduce inference spike activity by up to 88.6% while maintaining baseline classification accuracy. These results highlight the potential of V-MTJ/CMOS reconfigurable neurons to reduce computational activity and enable compact, energy-efficient brain-inspired computing systems.

cs.ET↗

arXiv2026

Octupole-driven spin torque switching of antiferromagnetic tunnel junctions

Magnetic tunnel junctions (MTJs) based on ferromagnets are canonical devices in spintronics, with wide-ranging applications in data storage, computing, and sensing. They simultaneously exhibit mechanisms for electrical detection and control of magnetic order through the tunneling magnetoresistance (TMR) and spin-transfer torque (STT) effects, respectively. It was long assumed that neither of these effects could be sizeable in all-antiferromagnetic tunnel junctions (AATJs), since they exhibit no net magnetization. Recently, however, it was shown that AATJs based on chiral antiferromagnets do exhibit TMR due to their non-relativistic momentum-dependent spin polarization and cluster magnetic octupole moment (CMO), which are manifestations of their spin-split band structure. However, the reciprocal effect, i.e., the antiferromagnetic counterpart of STT, has been assumed non-existent due to the total electric current being spin-neutral. Here, we report nanoscale AATJs exhibiting this reciprocal effect, which we term octupole-driven spin-transfer torque (OTT). We demonstrate current-induced OTT switching of PtMn3|MgO|PtMn3 AATJs, exhibiting a TMR value of 363% at room temperature and switching current densities of the order of 10 MA/cm2. Our theoretical modeling explains the origin of OTT in terms of the imbalance between intra- and inter-sublattice spin currents across the AATJ, and equivalently, in terms of the non-zero net cluster octupole polarization of each PtMn3 layer. This work establishes a new materials platform for antiferromagnetic spintronics and provides a pathway towards deeply scaled magnetic memory and room-temperature terahertz technologies.

cond-mat.mtrl-sci↗

arXiv2026

Reconfigurable field-free spin Hall nano-oscillators enabled by crystallographic anisotropy in epitaxial Co/Pt

Spin Hall nano-oscillators (SHNOs) are nanoscale microwave sources for wireless communication, neuromorphic computing and oscillator-based Ising machines, but conventional devices require a global magnetic bias. Here we replace this bias through crystallographic anisotropy in epitaxial Co/Pt. Growth of hcp Co with its c-axis in the film plane produces an anisotropy field of about 0.36 T and enables field-free auto-oscillations above 10 GHz in nanoconstriction SHNOs. The active current polarity is selected by the remanent magnetization, providing nonvolatile reconfiguration of the oscillation state. Micro-focused Brillouin light scattering confirms that the nonlinear response is confined to the nanoconstriction region. Lithographic control of the angle between the current and anisotropy axes tunes the excitation threshold and drives two spectral branches from separated modes to a dominant single branch, consistent with mutual synchronization. These results establish epitaxial crystallographic anisotropy as a route to reconfigurable field-free spintronic oscillators and oscillator networks.

cond-mat.mes-hall↗

arXiv2026

A magneto-mechanical gyroscope with spintronic readout

Gyroscopes are essential elements in navigation, consumer electronics, robotics, and aerospace applications. Most micro electro-mechanical systems (MEMS) implementations rely on capacitive sensing mechanisms, which limit the dimensional scaling to the micrometer scale. In this work, we introduce a MEMS-like two-degree-of-freedom (2-DOF) gyroscope that exploits the rectification functionality of magnetic tunnel junctions (MTJs) as its readout mechanism and as the transducer of the mechanical dynamics. Experimentally characterized MTJs have been used to calibrate and perform an experiment-informed design of the magneto-mechanical model combining micromagnetic theory with 2-DOF mechanical equations. We demonstrated that the output is linear with angular rate, and that the proposed device is able to extract the angular rate in dynamic cases exploiting a homodyne demodulation approach. The results open a path towards a compact, complementary metal-oxide semiconductor (CMOS)-compatible readout pathway that relaxes reliance on tight capacitive gaps and motivates multi-physics designs of the device.

cond-mat.mes-hall↗

arXiv2026

A scalable and resource-efficient pipelined p-computer for probabilistic Ising machines

Probabilistic Ising machines (PIMs) based on probabilistic bits offer a hardware-friendly route to solve combinatorial optimization problems, but most digital implementations achieve high throughput by exploiting sparse interactions. This limits their applicability to dense problems, for which memory bandwidth and data movement become the dominant bottlenecks. Here, we show a resource-efficient pipelined Field-Programmable Gate Array architecture enabling high-throughput execution of fully-connected PIMs while maintaining scalability and modularity. This architecture design combines a deeply pipelined (>20 stages) probabilistic bit update path, which overlaps spin evaluation and local-field updates, with a bandwidth-aware on-chip memory organization for the coupling and bias matrices. The architecture supports 512 p-bits with 16-bit fixed-point coefficients and 1024 and 2048 p-bits with 10-bit and 2-bit coefficients, respectively, and operates at up to 300 MHz. At fixed degree of parallelization, it delivers an order-of-magnitude higher update rate than an optimized non-pipelined baseline, while improving the time-area trade-off for dense workloads. Validation on portfolio optimization and low-density parity-check decoding shows close agreement with software references and substantial reductions in time-to-solution relative to the non-pipelined design, establishing pipelining as an effective route to scalable digital probabilistic computing for dense optimization problems.

eess.SY↗

arXiv2026

P-dit Probabilistic Ising Machine for Solving the Quadratic Assignment Problem

Combinatorial optimization problems represent a wide range of real-world scenarios where complicated interactions make it difficult to find the best solution. One example is the quadratic assignment problem (QAP), which involves determining the optimal placement of facilities at set locations which minimizes the products of material flow and facility distance. This representation is descriptive of many real-world scenarios, including the aggregate transportation costs of a supply chain. In this work, a probabilistic Ising machine (PIM) approach is implemented using probabilistic d-dimensional variables (p-dits), which are generalized, multi-state and multi-dimensional extensions to probabilistic bits (p-bits). Each p-dit corresponds to a location and stochastically oscillates between facility assignments based on the influence of the other p-dits. We show that with the same runtime and CPU, the PIM finds the best-known solution on 95% of considered instances from the QAP Library dataset, compared to just 36% for the standard Gurobi solver. For the unique largest problem in the library, a 2 to 3 order-of-magnitude decrease is observed in the time needed to reach specific solution qualities. We also show parallelization of our PIM through GPU implementations. A comparison to state-of-the-art QAP solver algorithms shows that they are consistently outperformed by both CPU and GPU implementations of the p-dit Ising machine.

physics.app-ph↗

arXiv2026

Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems

Extremely large-scale multiple-input multiple-output (XL-MIMO) architectures are a key enabler of forthcoming 6G wireless communication networks by allowing high data rates through massive spatial multiplexing. Here, we approach these problems with physics-inspired unconventional computing based on Ising machines (IMs). For binary modulation, probabilistic IMs (PIMs) and oscillator-based IMs achieve optimal ML detection with systems up to 2048x2048 antennas with only 100 iterations, matching optimal sphere decoder performance for computationally treatable sizes and outperforming the minimum mean-square error (MMSE) industrial standard. For M-QAM up to 256, a generalized PIM-inspired framework, based on d-dimensional probabilistic variables (p-dits) that directly encode QAM symbols, shows low bit-error-rate across sizes up to 256x256 antennas, outperforming or matching MMSE with reduced algorithmic complexity. Unlike the binary mapping, the p-dit interaction matrix is independent of the QAM order, enabling adaptive MIMO modulation. These results show a promising scalable paradigm for XL MIMO detection in future 6G networks.

cs.IT↗

arXiv2026

A Comparative Study of Digital Memristor-Based Processing-In-Memory from a Device and Reliability Perspective

As data-intensive applications increasingly strain conventional computing systems, processing-in-memory (PIM) has emerged as a promising paradigm to alleviate the memory wall by minimizing data transfer between memory and processing units. This review presents the recent advances in both stateful and non-stateful logic techniques for PIM, focusing on emerging nonvolatile memory technologies such as resistive random-access memory (RRAM), phase-change memory (PCM), and magnetoresistive random-access memory (MRAM). Both experimentally demonstrated and simulated logic designs are critically examined, highlighting key challenges in reliability and the role of device-level optimization in enabling scalable and commercial viable PIM systems. The review begins with an overview of relevant logic families, memristive device types, and associated reliability metrics. Each logic family is then explored in terms of how it capitalizes on distinct device properties to implement logic techniques. A comparative table of representative device stacks and performance parameters illustrates trade-offs and quality indicators. Through this comprehensive analysis, the development of optimized, robust memristive devices for next-generation PIM applications is supported.

cs.ET↗

arXiv2025

Extended-variable probabilistic computing with p-dits

Ising machines can solve combinatorial optimization problems by representing them as energy minimization problems. A common implementation is the probabilistic Ising machine (PIM), which uses probabilistic (p-) bits to represent coupled binary spins. However, many real-world problems have complex data representations that do not map naturally into a binary encoding, leading to a significant increase in hardware resources and time-to-solution. Here, we describe a generalized spin model that supports an arbitrary number of spin dimensions, each with an arbitrary real component. We define the probabilistic d-dimensional bit (p-dit) as the base unit of a p-computing implementation of this model. We further describe two restricted forms of p-dits for specific classes of common problems and implement them experimentally on an application-specific integrated circuit (ASIC): (A) isotropic p-dits, which simplify the implementation of categorical variables resulting in ~34x performance improvement compared to a p-bit implementation on an example 3-partition problem. (B) Probabilistic integers (p-ints), which simplify the representation of numeric values and provide ~5x improvement compared to a p-bit implementation of an example integer linear programming (ILP) problem. Additionally, we report a field-programmable gate array (FPGA) p-int-based integer quadratic programming (IQP) solver which shows ~64x faster time-to-solution compared to the best of a series of state-of-the-art software solvers. The generalized formulation of probabilistic variables presented here provides a path to solving large-scale optimization problems on various hardware platforms including digital CMOS.

physics.app-ph↗

arXiv2025

Quantized Artificial Neural Networks Implemented with Spintronic Stochastic Computing

An Artificial Neural Network (ANN) inference involves matrix vector multiplications that require a very large number of multiply and accumulate operations, resulting in high energy cost and large device footprint. Stochastic computing (SC) offers a less resource-intensive ANN implementation and can be realized through stochastic-magnetic tunnel junctions (s-MTJ) that generate random numbers, where the energy barrier to switch between the up and down states is designed to be small. While s-MTJs have previously been used to implement SC-ANNs, these studies have been limited to architectures with continuously varying (analog) weights. We study the use of SC for matrix vector multiplication with quantized synaptic weights and outputs. We show that a quantized SC-ANN, implemented by using experimentally obtained s-MTJ bitstreams and using a limited number of discrete quantized states for both weights and hidden layer outputs in an ANN, can effectively reduce latency and energy consumption in SC compared to an analog implementation, while largely preserving accuracy. We implemented quantization with 5 and 11 quantized states, along with SC configured with stochastic bitstream lengths of 100 to 500 on neural networks with one and three hidden layers. Inference was performed on the MNIST dataset for both training with SC and without SC. Training with SC provided better accuracy for all cases. For the shortest bitstream of 100 bits, the highest accuracies were 92% for one hidden layer and over 96% for three hidden layers. The overall system attained its peak accuracy of 96.82% using a 400-bit stochastic bitstream with three hidden layers and demonstrated 9X improvement in latency to implement neuron activations and 2.6X improvement in energy consumption using the quantized SC approach compared to a similar s-MTJ based ANN architecture without quantization.

cond-mat.mes-hall↗

arXiv2025

High-performance and reliable probabilistic Ising machine based on simulated quantum annealing

Probabilistic computing with pbits is emerging as a computational paradigm for machine learning and for facing combinatorial optimization problems (COPs) with the so-called probabilistic Ising machines (PIMs). From a hardware point of view, the key elements that characterize a PIM are the random number generation, the nonlinearity, the network of coupled pbits, and the energy minimization algorithm. Regarding the latter, in this work we show that PIMs using the simulated quantum annealing (SQA) schedule exhibit better performance as compared to simulated annealing and parallel tempering in solving a number of COPs, such as maximum satisfiability problems, planted Ising problem, and travelling salesman problem. Additionally, we design and simulate the architecture of a fully connected CMOS based PIM able to run the SQA algorithm having a spin-update time of 8 ns with a power consumption of 0.22 mW. Our results also show that SQA increases the reliability and the scalability of PIMs by compensating for device variability at an algorithmic level enabling the development of their implementation combining CMOS with different technologies such as spintronics. This work shows that the characteristics of the SQA are hardware agnostic and can be applied in the co-design of any hybrid analog digital Ising machine implementation. Our results open a promising direction for the implementation of a new generation of reliable and scalable PIMs.

cond-mat.mes-hall↗

arXiv2024

Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source

Probabilistic Ising machines (PIMs) provide a path to solving many computationally hard problems more efficiently than deterministic algorithms on von Neumann computers. Stochastic magnetic tunnel junctions (S-MTJs), which are engineered to be thermally unstable, show promise as entropy sources in PIMs. However, scaling up S-MTJ-PIMs is challenging, as it requires fine control of a small magnetic energy barrier across large numbers of devices. In addition, non-spintronic components of S-MTJ-PIMs to date have been primarily realized using general-purpose processors or field-programmable gate arrays. Reaching the ultimate performance of spintronic PIMs, however, requires co-designed application-specific integrated circuits (ASICs), combining CMOS with spintronic entropy sources. Here we demonstrate an ASIC in 130 nm foundry CMOS, which implements integer factorization as a representative hard optimization problem, using PIM-based invertible logic gates realized with 1143 probabilistic bits. The ASIC uses stochastic bit sequences read from an adjacent voltage-controlled (V-) MTJ chip. The V-MTJs are designed to be thermally stable in the absence of voltage, and generate random bits on-demand in response to 10 ns pulses using the voltage-controlled magnetic anisotropy effect. We experimentally demonstrate the chip's functionality and provide projections for designs in advanced nodes, illustrating a path to millions of probabilistic bits on a single CMOS+V-MTJ chip.

physics.app-ph↗

arXiv2024

Voltage-Controlled Magnetic Tunnel Junction based ADC-less Global Shutter Processing-in-Pixel for Extreme-Edge Intelligence

The vast amount of data generated by camera sensors has prompted the exploration of energy-efficient processing solutions for deploying computer vision tasks on edge devices. Among the various approaches studied, processing-in-pixel integrates massively parallel analog computational capabilities at the extreme-edge, i.e., within the pixel array and exhibits enhanced energy and bandwidth efficiency by generating the output activations of the first neural network layer rather than the raw sensory data. In this article, we propose an energy and bandwidth efficient ADC-less processing-in-pixel architecture. This architecture implements an optimized binary activation neural network trained using Hoyer regularizer for high accuracy on complex vision tasks. In addition, we also introduce a global shutter burst memory read scheme utilizing fast and disturb-free read operation leveraging innovative use of nanoscale voltage-controlled magnetic tunnel junctions (VC-MTJs). Moreover, we develop an algorithmic framework incorporating device and circuit constraints (characteristic device switching behavior and circuit non-linearity) based on state-of-the-art fabricated VC-MTJ characteristics and extensive circuit simulations using commercial GlobalFoundries 22nm FDX technology. Finally, we evaluate the proposed system's performance on two complex datasets - CIFAR10 and ImageNet, showing improvements in front-end and communication energy efficiency by 8.2x and 8.5x respectively and reduction in bandwidth by 6x compared to traditional computer vision systems, without any significant drop in the test accuracy.

cs.AR↗

arXiv2024

Dynamics of domain walls motion driven by spin-orbit torque in antiferromagnets

Ultrafast dynamics of antiferromagnetic materials is an appealing feature for novel spintronic devices. Several experiments have shown that both, the static states and the dynamical behavior of the antiferromagnetic order, are strictly related to stabilization of domains and domain wall (DW) motion. Hence for a quantitative understanding of statics and dynamics of multidomain states in antiferromagnetic materials a full micromagnetic framework is necessary. Here, we use this model to study the antiferromagnetic DW motion driven by the spin-orbit torque. The main result is the derivation of analytical expressions for the DW width and velocity that exhibit a very good agreement with the numerical simulations in a wide range of parameters. We also find that a mechanism limiting the maximum applicable current in an antiferromagnetic racetrack memory is the continuous nucleation of the domains from the edge, which is qualitatively different from what is observed in ferromagnetic racetracks.

cond-mat.mes-hall↗

arXiv2023

All-antiferromagnetic electrically controlled memory on silicon featuring large tunneling magnetoresistance

Antiferromagnetic (AFM) materials are a pathway to spintronic memory and computing devices with unprecedented speed, energy efficiency, and bit density. Realizing this potential requires AFM devices with simultaneous electrical writing and reading of information, which are also compatible with established silicon-based manufacturing. Recent experiments have shown tunneling magnetoresistance (TMR) readout in epitaxial AFM tunnel junctions. However, these TMR structures were not grown using a silicon-compatible deposition process, and controlling their AFM order required external magnetic fields. Here we show three-terminal AFM tunnel junctions based on the noncollinear antiferromagnet PtMn3, sputter-deposited on silicon. The devices simultaneously exhibit electrical switching using electric currents, and electrical readout by a large room-temperature TMR effect. First-principles calculations explain the TMR in terms of the momentum-resolved spin-dependent tunneling conduction in tunnel junctions with noncollinear AFM electrodes.

physics.app-ph↗

arXiv2023

A magneto-mechanical accelerometer based on magnetic tunnel junctions

Accelerometers have widespread applications and are an essential component in many areas such as automotive, consumer electronics and industrial applications. Most commercial accelerometers are based on micro-electromechanical system (MEMS) that are limited in downscaling and power consumption. Spintronics-based accelerometers have been proposed as alternatives, however, current proposals suffer from design limitations that result in reliability issues and high cost. Here we propose spintronic accelerometers with magnetic tunnel junctions (MTJs) as building block, which map accelerations into a measurable voltage across the MTJ terminals. The device exploits elastic and dipolar coupling as a sensing mechanism and the spintronic diode effect for the direct read out of the acceleration. The proposed technology represents a potentially competitive and scalable solution to current capacitive MEMS-based approaches that could lead to a step forward in many of the commercial applications.

physics.app-ph↗

arXiv2022

Antiferromagnetic parametric resonance driven by voltage-controlled magnetic anisotropy

Voltage controlled magnetic anisotropy (VCMA) is a low-energy alternative to manipulate the ferromagnetic state, which has been recently considered also in antiferromagnets (AFMs). Here, we theoretically demonstrate that VCMA can be used to excite linear and parametric resonant modes in easy-axis AFMs with perpendicular anisotropy, thus opening the way for an efficient electrical control of the Neel vector, and for detection of high-frequency dynamics. Our work leads to two key results: (i) VCMA parametric pumping experiences the so-called exchange enhancement of the coupling efficiency and, thus, is 1-2 orders of magnitude more efficient than microwave magnetic fields or spin-orbit-torques, and (ii) it also allows for zero-field parametric resonance, which cannot be achieved by other parametric pumping mechanisms in AFMs with out-of-plane easy axis. Therefore, we demonstrate that the VCMA parametric pumping is the most promising method for coherent excitation and manipulation of AFM order in perpendicular easy-axis AFMs.

cond-mat.mes-hall↗

arXiv2021

Observation of current-induced switching in non-collinear antiferromagnetic IrMn$_3$ by differential voltage measurements

There is accelerating interest in developing memory devices using antiferromagnetic (AFM) materials, motivated by the possibility for electrically controlling AFM order via spin-orbit torques, and its read-out via magnetoresistive effects. Recent studies have shown, however, that high current densities create non-magnetic contributions to resistive switching signals in AFM/heavy metal (AFM/HM) bilayers, complicating their interpretation. Here we introduce an experimental protocol to unambiguously distinguish current-induced magnetic and nonmagnetic switching signals in AFM/HM structures, and demonstrate it in IrMn$_3$/Pt devices. A six-terminal double-cross device is constructed, with an IrMn$_3$ pillar placed on one cross. The differential voltage is measured between the two crosses with and without IrMn$_3$ after each switching attempt. For a wide range of current densities, reversible switching is observed only when write currents pass through the cross with the IrMn$_3$ pillar, eliminating any possibility of non-magnetic switching artifacts. Micromagnetic simulations support our findings, indicating a complex domain-mediated switching process.

cond-mat.mes-hall↗