arXiv subjects

Steven J. Koester

Publications and source records attributed to Steven J. Koester.

At least 19 recordsLinked to original sources

arXiv2025

WSe2 p-MOSFETs with Nb-Doped WS2 Contacts Deposited using Atomic Layer Deposition

WSe2 p-MOSFETs with Nb-doped WS2 contacts formed using atomic layer deposition are demonstrated. The devices are fabricated using a technique that aligns the contact metallization with the Nb-doped WS2 contacts using a selective oxidation process. Devices with source/drain spacing of 0.15 um have on-state current of 103 uA/um at VDS = -1 V at a channel carrier concentration of ~ 7.5 x 1012 cm-2. The results provide a promising CMOS-compatible pathway to create low-resistance contacts to 2D-channel transistors.

cond-mat.mtrl-sci↗

arXiv2025

Generalized energy band alignment model for van der Waals heterostructures with a charge spillage dipole

The energy band alignment at the interface of van der Waals heterostructures (vdWHs) is a key design parameter for next-generation electronic and optoelectronic devices. Although the Anderson and midgap models have been widely adopted for bulk semiconductor heterostructures, they exhibit severe limitations when applied to vdWHs, particularly for type-III systems. Based on first-principles calculations for approximately $10^3$ vdWHs, we demonstrate these traditional models miss a critical dipole arising from interlayer charge spillage. We introduce a generalized linear response (gLR) model that includes this dipole through a quantum capacitance term while remaining analytically compact. With only two readily computed inputs, the charge neutrality level offset and the sum of the isolated-layer bandgaps, the gLR reproduces DFT band line-ups with $r^2\sim$0.9 across type-I, II, and III stacks. Machine-learning feature analysis confirms that these two descriptors dominate the underlying physics, indicating the model is near-minimal and broadly transferable. The gLR framework therefore provides both mechanistic insight and a fast, accurate surrogate for high-throughput screening of the vast vdW heterostructure design space.

cond-mat.mtrl-sci↗

arXiv2025

Content Addressable Memory Design with Reference Resistor for Improved Search Resolution

Despite the parallel in-memory search capabilities of content addressable memories (CAMs), their use in applications is constrained by their limited resolution that worsens as they are scaled to larger arrays or advanced nodes. In this work we present experimental results for a novel back-end-of-line compatible reference resistive device that can significantly improve the search resolution of CAMs implemented with CMOS and beyond-CMOS technologies to less than or equal to 5-bits.

cs.ET↗

arXiv2025

Ferroelectric Switching in Hybrid Molecular Beam Epitaxy-Grown BaTiO3 Films

Molecular beam epitaxy (MBE) is a promising synthesis technique for both heterostructure growth and epitaxial integration of ferroelectric BaTiO3. However, a direct measurement of the remnant polarization (P_r) has not been previously reported in MBE-grown BaTiO3 films. We report the in-situ growth of an all-epitaxial SrRuO3/BaTiO3/SrRuO3 heterostructure on Nb-doped SrTiO3 (001) substrates by hybrid MBE using metal-organic precursors. This capacitor structure consisting of 16 nm SrRuO3/40 nm BaTiO3/16 nm SrRuO3 shows hysteretic polarization-electric field (P-E) curves with P_r = 15 μC cm-2 at frequencies ranging from 500 Hz to 20 kHz, after isolating the intrinsic ferroelectric response from non-ferroelectric contributions using the Positive-Up-Negative-Down (PUND) method. We hypothesize that the asymmetry in switching behavior and current leakage has origins in structural defects.

cond-mat.mtrl-sci↗

arXiv2024

Dielectrophoresis-Enhanced Graphene Field-Effect Transistors for Nano-Analyte Sensing

Dielectrophoretic (DEP) sensing is an extremely important sensing modality that enables the rapid capture and detection of polarizable particles of nano-scale size. This makes it a versatile tool for applications in medical diagnostics, environmental monitoring, and materials science. Because DEP relies upon the creation of sharp electrode edges, its sensitivity is fundamentally limited by the electrode thickness. Graphene, with its monolayer thickness, enables scaling of the DEP force, allowing trapping of particles at graphene edges at ultra-low voltages. However, to date, this enhanced trapping efficiency of graphene has not been translated into an effective sensing geometry. Here, we demonstrate the expansion of graphene DEP trapping capability into a graphene field effect transistor (GFET) geometry that allows the trapped particles to be electrically detected. This four-terminal multi-functional hybrid device structure operates in three distinct modes: DEP, GFET, and DEP-GFET. By segmenting the channel of the GFET into multiple parallel channels, greatly increased density of particle trapping is demonstrated using fluorescence microscopy analysis. We show further enhancement of the trapping efficiency using engineered "nano-sites," which are holes in the graphene with size on the order of 200-300 nm. Scanning electron microscope analysis of immobilized gold nanoparticles (AuNPs) shows trapping efficiency >90% for properly engineered nano-sites. Using nano-site trapping, we also demonstrate real-time, rapid electrical sensing of AuNPs, with >2% current change occurring in 4.1 seconds, as well as rapid sensing of a variety of biomolecule-coated nanoparticles. This work shows that graphene DEP is an effective platform for nanoparticle and bio-molecule sensing that overcomes diffusion-limited and Brownian motion-based interactions.

cond-mat.mes-hall↗

arXiv2023

Fast and Facile Synthesis Route to Epitaxial Oxide Membrane Using a Sacrificial Layer

The advancement in thin-film exfoliation for synthesizing oxide membranes has opened up new possibilities for creating artificially-assembled heterostructures with structurally and chemically incompatible materials. The sacrificial layer method is a promising approach to exfoliate as-grown films from a compatible material system, allowing their integration with dissimilar materials. Nonetheless, the conventional sacrificial layers often possess intricate stoichiometry, thereby constraining their practicality and adaptability, particularly when considering techniques like Molecular Beam Epitaxy (MBE). This is where easy-to-grow binary alkaline earth metal oxides with a rock salt crystal structure are useful. These oxides, which include (Mg, Ca, Sr, Ba)O, can be used as a sacrificial layer covering a much broader range of lattice parameters compared to conventional sacrificial layers and are easily dissolvable in deionized water. In this study, we show the epitaxial growth of single-crystalline perovskite SrTiO3 (STO) on sacrificial layers consisting of crystalline SrO, BaO, and Ba1-xCaxO films, employing a hybrid MBE method. Our results highlight the rapid (< 5 minutes) dissolution of the sacrificial layer when immersed in deionized water, facilitating the fabrication of millimeter-sized STO membranes. Using high-resolution x-ray diffraction, atomic-force microscopy, scanning transmission electron microscopy, impedance spectroscopy, and scattering-type near-field optical microscopy (SNOM), we demonstrate epitaxial STO membranes with bulk-like intrinsic dielectric properties. The employment of alkaline earth metal oxides as sacrificial layers is likely to simplify membrane synthesis, particularly with MBE, thus expanding research possibilities.

physics.app-ph↗

arXiv2022

How to Report and Benchmark Emerging Field-Effect Transistors

Emerging low-dimensional nanomaterials have been studied for decades in device applications as field-effect transistors (FETs). However, properly reporting and comparing device performance has been challenging due to the involvement and interlinking of multiple device parameters. More importantly, the interdisciplinarity of this research community results in a lack of consistent reporting and benchmarking guidelines. Here we report a consensus among the authors regarding guidelines for reporting and benchmarking important FET parameters and performance metrics. We provide an example of this reporting and benchmarking process for a two-dimensional (2D) semiconductor FET. Our consensus will help promote an improved approach for assessing device performance in emerging FETs, thus aiding the field to progress more consistently and meaningfully.

cond-mat.mtrl-sci↗

arXiv2022

Free-Standing Epitaxial SrTiO$_3$ Nanomembranes via Remote Epitaxy using Hybrid Molecular Beam Epitaxy

The epitaxial growth of functional materials using a substrate with a graphene layer is a highly desirable method for improving structural quality and obtaining free-standing epitaxial nano-membranes for scientific study, applications, and economical reuse of substrates. However, the aggressive oxidizing conditions typically employed to grow epitaxial perovskite oxides can damage graphene. Here, we demonstrate a technique based on hybrid molecular beam epitaxy that does not require an independent oxygen source to achieve epitaxial growth of complex oxides without damaging the underlying graphene. The technique produces films with self-regulating cation stoichiometry control and epitaxial orientation to the oxide substrate. Furthermore, the films can be exfoliated and transferred to foreign substrates while leaving the graphene on the original substrate. These results open the door to future studies of previously unattainable free-standing nano-membranes grown in an adsorption-controlled manner by hybrid molecular beam epitaxy, and has potentially important implications for the commercial application of perovskite oxides in flexible electronics.

cond-mat.mtrl-sci↗

arXiv2020

Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation

In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification. The method employs a reinforcement signal that mimics backpropagation but is far less computationally intensive. The weight update calculation at each layer requires only local data apart from this signal. We also employ a rule capable of producing specific output spike trains; by setting the target spike time equal to the actual spike time with a slight negative offset for key high-value neurons the actual spike time becomes as early as possible. In simulated MNIST handwritten digit classification, two-layer networks trained with this rule matched the performance of a comparable backpropagation based non-spiking network.

cs.NE↗

arXiv2020

SHE-MTJ Circuits for Convolutional Neural Networks

We report the performance characteristics of a notional Convolutional Neural Network based on the previously-proposed Multiply-Accumulate-Activate-Pool set, an MTJ-based spintronic circuit made to compute multiple neural functionalities in parallel. A study of image classification with the MNIST handwritten digits dataset using this network is provided via simulation. The effect of changing the weight representation precision, the severity of device process variation within the MAAP sets and the computational redundancy are provided. The emulated network achieves between 90 and 95\% image classification accuracy at a cost of ~100 nJ per image.

cs.ET↗

arXiv2020

Spin-Hall MTJ Cells for Intra-Column Competition in Hierarchical Temporal Memory

We propose a dedicated winner-take-all circuit to efficiently implement the intra-column competition between cells in Hierarchical Temporal Memory which is a crucial part of the algorithm. All inputs and outputs are charge-based for compatibility with standard CMOS. The circuit incorporates memristors for competitive advantage to emulate a column with a cell in a predictive state. The circuit can also detect columns 'bursting' by passive averaging and comparison of the cell outputs. The proposed spintronic devices and circuit are thoroughly described and a series of simulations are used to predict the performance. The simulations indicate that the circuit can complete a nine-cell, nine-input competition operation in under 15 ns at a cost of about 25 pJ.

cs.ET↗

arXiv2020

Layer-dependence of dielectric response and water-enhanced ambient degradation of highly-anisotropic black As

Black arsenic (BAs) is a van der Waals layered material with a puckered honeycomb structure and has received increased interest due to its anisotropic properties and promising performance in devices. Here, crystalline structure, thickness-dependent dielectric responses, and ambient stability of BAs nanosheets are investigated using STEM imaging and spectroscopy. Atomic-resolution HAADF-STEM images directly visualize the three-dimensional structure and evaluate the degree of anisotropy. STEM-EELS is used to measure the dielectric response of BAs as a function of the number of layers. Finally, BAs degradation under different ambient environments is studied highlighting high sensitivity to moisture in the air.

cond-mat.mtrl-sci↗

arXiv2019

Nonvolatile Spintronic Memory Cells for Neural Networks

A new spintronic nonvolatile memory cell analogous to 1T DRAM with non-destructive read is proposed. The cells can be used as neural computing units. A dual-circuit neural network architecture is proposed to leverage these devices against the complex operations involved in convolutional networks. Simulations based on HSPICE and Matlab were performed to study the performance of this architecture when classifying images as well as the effect of varying the size and stability of the nanomagnets. The spintronic cells outperform a purely charge-based implementation of the same network, consuming about 100 pJ total per image processed.

cs.ET↗

arXiv2019

Convolutional Neural Networks Utilizing Multifunctional Spin-Hall MTJ Neurons

We propose a new network architecture for standard spin-Hall magnetic tunnel junction-based spintronic neurons that allows them to compute multiple critical convolutional neural network functionalities simultaneously and in parallel, saving space and time. An approximation to the Rectified Linear Unit transfer function and the local pooling function are computed simultaneously with the convolution operation itself. A proof-of-concept simulation is performed on the MNIST dataset, achieving up to 98% accuracy at a cost of less than 1 nJ for all convolution, activation and pooling operations combined. The simulations are remarkably robust to thermal noise, performing well even with very small magnetic layers.

cs.ET↗

arXiv2018

Benchmarking Inverse Rashba-Edelstein Magnetoelectric Devices for Neuromorphic Computing

We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This combination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.

cs.ET↗

arXiv2018

An analytic virtual-source-based current-voltage model for ultra-thin black phosphorus field-effect transistors

In this paper, we develop an analytic physics-based model to describe current conduction in ultra-thin black phosphorus (BP) field-effect transistors (FETs). The model extends the concept of virtual source charge calculation to capture the effect of both hole and electron charges for ambipolar transport characteristics. The model comprehends the in-plane band-structure anisotropy in BP, as well as the asymmetry in electron and hole current conduction characteristics. The model also includes the effect of Schottky-type source/drain contact resistances, which are voltage-dependent and can significantly limit current conduction in the on-state in BP FETs. Model parameters are extracted using measured data of back-gated BP transistors with gate lengths of 1000 nm and 300 nm with BP thickness of 7.3 nm and 8.1 nm, and for the temperature range of 180 K to 298 K. Compared to previous BP models that are validated only for room-temperature and near-equilibrium bias conditions (low drain-source voltage), we demonstrate excellent agreement between the data and model over a broad range of bias and temperature values. The model is also validated against numerical TCAD data of top-gated BP transistors with a channel length of 300 nm. The model is implemented in Verilog-A and the capability of the model to handle both dc and transient circuit simulations is demonstrated using SPECTRE. The model not only provides a physical insight into technology-device interaction in BP transistors, but can also be used to design and optimize BP-based circuits using a standard hierarchical circuit simulator.

physics.app-ph↗

arXiv2018

An Inside Look at the Ti-MoS2 Contact in Ultra-thin Field Effect Transistor with Atomic Resolution

Two-dimensional molybdenum disulfide (MoS2) is an excellent channel material for ultra-thin field effect transistors. However, high contact resistance across the metal-MoS2 interface continues to limit its widespread realization. Here, using atomic-resolution analytical scanning transmission electron microscopy (STEM) together with first principle calculations, we show that this contact problem is a fundamental limitation from the bonding and interactions at the metal-MoS2 interface that cannot be solved by improved deposition engineering. STEM analysis in conjunction with theory shows that when MoS2 is in contact with Ti, a metal with a high affinity to form strong bonds with sulfur, there is a release of S from Mo along with the formation of small Ti/TixSy clusters. A destruction of the MoS2 layers and penetration of metal can also be expected. The design of true high-mobility metal-MoS2 contacts will require the optimal selection of the metal or alloy based on their bonding interactions with the MoS2 surface. This can be advanced by evaluation of binding energies with increasing the number of atoms within metal clusters.

cond-mat.mtrl-sci↗

arXiv2018

Spin absorption by in situ deposited nanoscale magnets on graphene spin valves

An in situ measurement of spin transport in a graphene nonlocal spin valve is used to quantify the spin current absorbed by a small (250 nm $\times$ 750 nm) metallic island. The experiment allows for successive depositions of either Fe or Cu without breaking vacuum, so that the thickness of the island is the only parameter that is varied. Furthermore, by measuring the effect of the island using separate contacts for injection and detection, we isolate the effect of spin absorption from any change in the spin injection and detection mechanisms. As inferred from the thickness dependence, the effective spin current $j_e = \frac{2e}{\hbar} j_s$ absorbed by Fe is as large as $10^8$ A/m$^2$. The maximum value of $j_e$ is limited by the resistance-area product of the graphene/Fe interface, which is as small as 3 $Ωμ$m$^2$. The spin current absorbed by the same thickness of Cu is smaller than for Fe, as expected given the longer spin diffusion length and larger spin resistance of Cu compared to Fe. These results allow for a quantitative assessment of the prospects for achieving spin transfer torque switching of a nanomagnet using a graphene-based nonlocal spin valve.

cond-mat.mes-hall↗