arXiv subjects

Sayeef Salahuddin

Publications and source records attributed to Sayeef Salahuddin.

At least 19 recordsLinked to original sources

arXiv2026

multi-Stochastic Core Architecture for Scaling Probabilistic Ising Machines

Ising Machines offer vast potential to solve NP-hard optimization problems efficiently that are intractable to solve using conventional computing architecture. A lot of these optimization problems fall into statistical learnability and involve finding an optimal solution among many possible, near-analogous configurations, by searching in a non-convex energy landscape. In this context, the probabilistic Boltzmann machine architecture especially PASS (Parallel Asynchronous Stochastic Sampler), explores and models the complex probability landscape pertaining to all possible configurations and excels in finding the ground-state energy solution of these intractable problems. Additionally, the noise-based neuron architecture addresses the limitation of conventional annealing methods, which may get stuck around local minima. Here, we demonstrate a stochastic sampling approach based on Block Gibbs Sampling to integrate multiple asynchronous PASS chips (four in this work), enabling improved scalability. Further, we demonstrate the scaling by mapping 784 nodes Max-Cut problem integrating 256 nodes PASS accelerator manufactured in 14 nm CMOS FinFET technology. PASS-enabled system with Block Gibbs Sampling protocol shows approximately 1000 times speedup for Max-Cut optimization compared to state-of-the-art methods implemented on CPUs and GPUs. The general applicability of this approach is further illustrated by solving a quantum spin chain Transverse Ising system and accurately representing complex probability landscapes. Moreover, our results demonstrate the change in the scaling law to constant in the scaled-PASS accelerator as compared to exponential on GPUs enabling at least 4 orders of magnitude improvement in time-to-solution. Hence, the presented methodology enables the pathway for scaling of asynchronous brain-like dynamics systems that do not follow any clock for its operation.

cs.ET↗

arXiv2025

Hardware Acceleration of Frustrated Lattice Systems using Convolutional Restricted Boltzmann Machine

Geometric frustration gives rise to emergent quantum phenomena and exotic phases of matter. While Monte Carlo methods are traditionally used to simulate such systems, their sampling efficiency is limited by the complexity of interactions and ground-state properties. Restricted Boltzmann Machines (RBMs), a class of probabilistic neural networks, offer improved sampling by incorporating machine learning techniques. However, fully-connected bipartite RBMs are inefficient for representing physical lattices with sparse interactions. To address this, we implement Convolutional Restricted Boltzmann Machines (CRBMs) that leverage translational symmetry inherent to lattices. Using the classical Shastry-Sutherland (SS) Ising lattice, we demonstrate (i) CRBM formulation that captures SS interactions, and (ii) digital hardware accelerator to enhance sampling performance. We simulate lattices with up to 324 spins, recovering all known phases of the SS Ising model, including the long range ordered fractional plateau. Our hardware characterizes spin behavior at critical points and within spin liquid phases. This implementation achieves a speedup of 3 to 5 orders of magnitude (33 ns to 120 ms) over GPU-based implementations. Moreover, the time-to-solution is within two orders of magnitude of quantum annealers, while offering superior scalability, room-temperature operation and reprogrammability. This work paves a pathway for scalable digital hardware that embeds physical symmetries to enable large scale simulations of material systems.

cond-mat.stat-mech↗

arXiv2025

Negative capacitance overcomes Schottky-gate limits in GaN high-electron-mobility transistors

For high-electron-mobility transistors based on two-dimensional electron gas (2DEG) within a quantum well, such as those based on AlGaN/GaN heterostructure, a Schottky-gate is used to maximize the amount of charge that can be induced and thereby the current that can be achieved. However, the Schottky-gate also leads to very high leakage current through the gate electrode. Adding a conventional dielectric layer between the nitride layers and gate metal can reduce leakage; but this comes at the price of a reduced drain current. Here, we used a ferroic HfO2-ZrO2 bilayer as the gate dielectric and achieved a simultaneous increase in the ON current and decrease in the leakage current, a combination otherwise not attainable with conventional dielectrics. This approach surpasses the conventional limits of Schottky GaN transistors and provides a new pathway to improve performance in transistors based on 2DEG.

cond-mat.mtrl-sci↗

arXiv2025

Efficient Optimization Accelerator Framework for Multistate Ising Problems

Ising Machines are emerging hardware architectures that efficiently solve NP-Hard combinatorial optimization problems. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form, but this transformation often complicates the solution landscape, degrading performance, especially for multi-state problems. To address this challenge, we model spin interactions as generalized boolean logic function to significantly reduce the exploration space. We demonstrate the effectiveness of our approach on graph coloring problem using probabilistic Ising solvers, achieving similar accuracy compared to state-of-the-art heuristics and machine learning algorithms. It also shows significant improvement over state-of-the-art QUBO-based Ising solvers, including probabilistic Ising and simulated bifurcation machines. We also design 1024-neuron all-to-all connected probabilistic Ising accelerator on FPGA with the proposed approach that shows ~10000x performance acceleration compared to GPU-based Tabucol heuristics and reducing physical neurons by 1.5-4x over baseline Ising frameworks. Thus, this work establishes superior efficiency, scalability and solution quality for multi-state optimization problems.

cs.AR↗

arXiv2025

Colossal enhancement of spin transmission through magnon confinement in an antiferromagnet

Since Felix Bloch's introduction of the concept of spin waves in 1930, magnons (the quanta of spin waves) have been extensively studied in a range of materials for spintronics, particularly for non-volatile logic-in-memory devices. Controlling magnons in conventional antiferromagnets and harnessing them in practical applications, however, remains a challenge. In this letter, we demonstrate highly efficient magnon transport in an LaFeO$_3$/BiFeO$_3$/LaFeO$_3$ all-antiferromagnetic system which can be controlled electrically, making it highly desirable for energy-efficient computation. Leveraging spin-orbit-driven spin-charge transduction, we demonstrate that this material architecture permits magnon confinement in ultrathin antiferromagnets, enhancing the output voltage generated by magnon transport by several orders of magnitude, which provides a pathway to enable magnetoelectric memory and logic functionalities. Additionally, its non-volatility enables ultralow-power logic-in-memory processing, where magnonic devices can be efficiently reconfigured via electrically controlled magnon spin currents within magnetoelectric channels.

cond-mat.mtrl-sci↗

arXiv2024

Symmetry-designed BiFeO3 single domain spin cycloid for efficient spintronics

Deterministic control of coupled ferroelectric and antiferromagnetic orders remains a central challenge in multiferroics, limiting their integration into functional magnetoelectrics and magnonic-devices. (111)pc BiFeO3 with a robust single spin cycloid, offers direct magnetoelectric-coupling and a platform for efficient spin transport, yet multi-magnetic domains and ferroelectric-fatigue have prevented reproducible control. Here, we show that anisotropic-compressive in-plane strain stabilizes a single antiferromagnetic domain with unique spin-cycloid vector, by breaking the symmetry of the (111)pc plane. Epitaxial BiFeO3 films grown on orthorhombic NdGaO3 (011)o [(111)pc] substrates impose the required anisotropic in-plane strain and stabilizes single antiferromagnetic domain, as confirmed through direct imaging with scanning NV microscopy and non-resonant-x-ray-magnetic-scattering. Remarkably, these engineered films exhibit deterministic and non-volatile 180{\deg} switching of ferroelectric and single antiferromagnetic domains over 1,000 cycles. The monodomain state also enables anisotropic and threefold enhanced magnon transport with reduced scattering. Thus, symmetry-designed (111)pc monodomain BiFeO3 offers a robust platform for advanced magnetoelectric and magnonic applications.

cond-mat.mtrl-sci↗

arXiv2024

Coherent Dipolar Coupling between Magnetoelastic Waves and Nitrogen Vacancy Centers

We experimentally demonstrate coherent Rabi oscillations of Nitrogen Vacancy (NV) centers by magnetoelastic waves. The coupling is consistent with dipolar stray field drive from spin-wave modes in a ferromagnetic film, and displays a significant improvement in Radio Frequency power efficiency relative to other methods of microwave excitation. Further, it demonstrates coherent coupling with NV centers over mm-scale distances from the microwave excitation source. By utilizing a piezoelectric-magnetostrictive heterostucture, where magnetoelastic waves can be launched by an applied voltage, a pure voltage driven coherent drive of the NV centers is achieved. This voltage driven, magnetoelastic excitation enables a new approach to couple with two level quantum states that is not reliant on long spin-wave coherence lengths.

cond-mat.mes-hall↗

arXiv2024

PASS: An Asynchronous Probabilistic Processor for Next Generation Intelligence

New computing paradigms are required to solve the most challenging computational problems where no exact polynomial time solution exists.Probabilistic Ising Accelerators has gained promise on these problems with the ability to model complex probability distributions and find ground states of intractable problems. In this context, we have demonstrated the Parallel Asynchronous Stochastic Sampler (PASS), the first fully on-chip integrated, asynchronous, probabilistic accelerator that takes advantage of the intrinsic fine-grained parallelism of the Ising Model and built in state of the art 14nm CMOS FinFET technology. We have demonstrated broad applicability of this accelerator on problems ranging from Combinatorial Optimization, Neural Simulation, to Machine Learning along with up to $23,000$x energy to solution improvement compared to CPUs on probabilistic problems.

cs.DC↗

arXiv2024

Non-volatile spin transport in a single domain multiferroic

Antiferromagnets have attracted significant attention in the field of magnonics, as promising candidates for ultralow-energy carriers for information transfer for future computing. The role of crystalline orientation distribution on magnon transport has received very little attention. In multiferroics such as BiFeO$_3$ the coupling between antiferromagnetic and polar order imposes yet another boundary condition on spin transport. Thus, understanding the fundamentals of spin transport in such systems requires a single domain, a single crystal. We show that through Lanthanum(La) substitution, a single ferroelectric domain can be engineered with a stable, single-variant spin cycloid, controllable by an electric field. The spin transport in such a single domain displays a strong anisotropy, arising from the underlying spin cycloid lattice. Our work shows a pathway to understand the fundamental origins of spin transport in such a single domain multiferroic.

cond-mat.mtrl-sci↗

arXiv2024

3D ferroelectric phase field simulations of polycrystalline multi-phase hafnia and zirconia based ultra-thin films

HfO$_2$- and ZrO$_2$-based ferroelectric thin films have emerged as promising candidates for the gate oxides of next generation electronic devices. Recent work has experimentally demonstrated that a tetragonal/orthorhombic (t/o-) phase mixture with partially in-plane polarization can lead to negative capacitance (NC) stabilization. However, there is a discrepancy between experiments and the theoretical understanding of domain formation and domain wall motion in these multi-phase, polycrystalline materials. Furthermore, the effect of anisotropic domain wall coupling on NC has not been studied so far. Here we apply 3D phase field simulations of HfO$_2$- and ZrO$_2$-based mixed-phase ultra-thin films on silicon to understand the necessary and beneficial conditions for NC stabilization. We find that smaller ferroelectric grains and a larger angle of the polar axis with respect to the out-of-plane direction enhances the NC effect. Furthermore, we show that theoretically predicted negative domain wall coupling even along only one axis prevents NC stabilization. Therefore, we conclude that topological domain walls play a critical role in experimentally observed NC phenomena in HfO$_2$- and ZrO$_2$-based ferroelectrics.

cond-mat.mes-hall↗

arXiv2023

Emergent Classical Spin Liquid Phases in an Ising Lattice via Size Effects

We show that a classical spin liquid phase can emerge from an ordered magnetic state in the two-dimensional frustrated Shastry-Sutherland Ising lattice due to lateral confinement. Two distinct classical spin liquid states are stabilized (i) long-range spin-correlated dimers, and (ii) exponentially decaying spin-correlated disordered states, depending on widths of W=3n, 3n+1 or W=3n+2, n being a positive integer. Stabilization of spin liquids in a square-triangular lattice moves beyond the conventional geometric paradigm of kagome, triangular or tetrahedral arrangements of antiferromagnetic ions, where spin liquids have been discussed conventionally.

cond-mat.stat-mech↗

arXiv2023

Accelerated Modelling of Interfaces for Electronic Devices using Graph Neural Networks

Modern microelectronic devices are composed of interfaces between a large number of materials, many of which are in amorphous or polycrystalline phases. Modeling such non-crystalline materials using first-principles methods such as density functional theory is often numerically intractable. Recently, graph neural networks (GNNs) have shown potential to achieve linear complexity with accuracies comparable to ab-initio methods. Here, we demonstrate the applicability of GNNs to accelerate the atomistic computational pipeline for predicting macroscopic transistor transport characteristics via learning microscopic physical properties. We generate amorphous heterostructures, specifically the HfO$_{2}$-SiO$_{2}$-Si semiconductor-dielectric transistor gate stack, via GNN predicted atomic forces, and show excellent accuracy in predicting transport characteristics including injection velocity for nanoslab silicon channels. This work paves the way for faster and more scalable methods to model modern advanced electronic devices via GNNs.

cond-mat.mtrl-sci↗

arXiv2023

Manipulating chiral-spin transport with ferroelectric polarization

A collective excitation of the spin structure in a magnetic insulator can transmit spin-angular momentum with negligible dissipation. This quantum of a spin wave, introduced more than nine decades ago, has always been manipulated through magnetic dipoles, (i.e., timereversal symmetry). Here, we report the experimental observation of chiral-spin transport in multiferroic BiFeO3, where the spin transport is controlled by reversing the ferroelectric polarization (i.e., spatial inversion symmetry). The ferroelectrically controlled magnons produce an unprecedented ratio of up to 18% rectification at room temperature. The spin torque that the magnons in BiFeO3 carry can be used to efficiently switch the magnetization of adja-cent magnets, with a spin-torque efficiency being comparable to the spin Hall effect in heavy metals. Utilizing such a controllable magnon generation and transmission in BiFeO3, an alloxide, energy-scalable logic is demonstrated composed of spin-orbit injection, detection, and magnetoelectric control. This observation opens a new chapter of multiferroic magnons and paves an alternative pathway towards low-dissipation nanoelectronics.

physics.app-ph↗

arXiv2022

Topological Edge Conduction Induced by Strong Anisotropic Exchange Interactions

We predict that an interplay between isotropic and anisotropic exchange interactions in a honeycomb lattice structure can lead to topological edge conduction when the anisotropic interaction is at least twice the strength of the isotropic interaction. For materials like Na$_2$IrO$_3$, such a strong anisotropic exchange interaction simultaneously induces a zigzag type of antiferromagnetic order that breaks the time-reversal symmetry of the topological edge conductor. We show that the electronic transport in such topological conductors will exhibit a quantized Hall conductance without any external magnetic field when the Fermi energy lies within a particular energy range.

cond-mat.mes-hall↗

arXiv2022

FerroX : A GPU-accelerated, 3D Phase-Field Simulation Framework for Modeling Ferroelectric Devices

We present a massively parallel, 3D phase-field simulation framework for modeling ferro-electric materials based scalable logic devices. We self-consistently solve the time-dependent Ginzburg Landau (TDGL) equation for ferroelectric polarization, Poisson equation for electric potential, and charge equation for carrier densities in semiconductor regions. The algorithm is implemented using the AMReX software framework, which provides effective scalability on manycore and GPU-based supercomputing architectures. We demonstrate the performance of the algorithm with excellent scaling results on NERSC multicore and GPU systems, with a significant (15x) speedup on the GPU using a node-by-node comparison. We further demonstrate the applicability of the code in simulations of ferroelectric domain-wall induced negative capacitance (NC) effect in Metal-Ferroelectric-Insulator-Metal (MFIM) and Metal-Ferroelectric-Insulator-Semiconductor-Metal (MFISM) devices. The charge (Q) v.s. applied voltage (V) responses for these structures clearly indicates stabilized negative capacitance.

cs.CE↗

arXiv2022

Innovating at Speed and at Scale: A Next Generation Infrastructure for Accelerating Semiconductor Technologies

Semiconductor innovation drives improvements to technologies that are critical to modern society. The country that successfully accelerates semiconductor innovation is positioned to lead future semiconductor-driven industries and benefit from the resulting economic growth. It is our view that a next generation infrastructure is necessary to accelerate and enhance semiconductor innovation in the U.S. In this paper, we propose such an advanced infrastructure composed of a national network of facilities with enhancements in technology and business models. These enhancements enable application-driven and challenge-based research and development, and ensure that facilities are accessible and sustainable. The main tenets are: a challenge-driven operational model, a next-generation infrastructure to serve that operational model, technology innovations needed for advanced facilities to speed up learning cycles, and innovative cost-effective business models for sustainability. Ultimately, the expected outcomes of such a participatory, scalable, and sustainable nation-level advanced infrastructure will have tremendous impact on government, industry, and academia alike.

cs.OH↗

arXiv2021

Unified Framework for Charge-Spin Interconversion in Spin-Orbit Materials

Materials with spin-orbit coupling are of great interest for various spintronics applications due to the efficient electrical generation and detection of spin-polarized electrons. Over the past decade, many materials have been studied, including topological insulators, transition metals, Kondo insulators, semimetals, semiconductors, and oxides; however, there is no unifying physical framework for understanding the physics and therefore designing a material system and devices with the desired properties. We present a model that binds together the experimental data observed on the wide variety of materials in a unified manner. We show that in a material with a given spin-momentum locking, the density of states plays a crucial role in determining the charge-spin interconversion efficiency, and a simple inverse relationship can be obtained. Remarkably, experimental data obtained over the last decade on many different materials closely follow such an inverse relationship. We further deduce two figure-of-merits of great current interest: the spin-orbit torque (SOT) efficiency (for the direct effect) and the inverse Rashba-Edelstein effect length (for the inverse effect), which statistically show good agreement with the existing experimental data on wide varieties of materials. Especially, we identify a scaling law for the SOT efficiency with respect to the carrier concentration in the sample, which agrees with existing data. Such an agreement is intriguing since our transport model includes only Fermi surface contributions and fundamentally different from the conventional views of the SOT efficiency that includes contributions from all the occupied states.

cond-mat.mes-hall↗

arXiv2021

Ferroelectric HfO$_2$ Memory Transistors with High-$κ$ Interfacial Layer and Write Endurance Exceeding $10^{10}$ Cycles

We demonstrate ferroelectric (FE) memory transistors on a crystalline silicon channel with endurance exceeding $10^{10}$ cycles. The ferroelectric transistors (FeFETs) incorporate a high-$κ$ interfacial layer (IL) of thermally grown silicon nitride (SiN$_x$) and a thin 4.5 nm layer of Zr-doped FE-HfO$_2$ on a $\sim$30 nm SOI channel. The device shows a $\sim$ 1V memory window in a DC sweep of just $\pm$ 2.5V, and can be programmed and erased with voltage pulses of $V_G= \pm$ 3V at a pulse width of 250 ns. The device also shows very good retention behavior. These results indicate that appropriate engineering of the IL layer could substantially improve FeFET device performance and reliability.

physics.app-ph↗