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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., Comment: 36 pages, 4 figures

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., Comment: 20 pages, 6 figures

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., Comment: 15 pages, 3 figures

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.

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.

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.

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., Comment: 26 pages, 5 figures

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.

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.

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., Comment: 8 pages, 6 figures

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.

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.

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 $\Omega\mu$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., Comment: 20 pages, includes supplemental information

A graphene-based spin-diffusive (GrSD) neural network is presented in this work that takes advantage of the locally tunable spin transport of graphene and the non-volatility of nanomagnets. By using electrostatically gated graphene as spintronic synapses, a weighted summation operation can be performed in the spin domain while the weights can be programmed using circuits in the charge domain. Four-component spin/charge circuit simulations coupled to magnetic dynamics are used to show the feasibility of the neuron-synapse functionality and quantify the analog weighting capability of the graphene under different spin relaxation mechanisms. By realizing transistor-free weight implementation, the graphene spin-diffusive neural network reduces the energy consumption to 0.08-0.32 fJ per cell-synapse and achieves significantly better scalability compared to its digital counterparts, particularly as the number and bit accuracy of the synapses increases., Comment: 8 pages, 12 figures

Contact resistance is a severe performance bottleneck for electronic devices based on two-dimensional layered (2D) semiconductors, whose contacts are Schottky rather than Ohmic. Although there is general consensus that the injection mechanism changes from thermionic to tunneling with gate biasing, existing models tend to oversimplify the transport problem, by neglecting the 2D transport nature and the modulation of the Schottky barrier height, the latter being of particular importance in back-gated devices. In this work, we develop a semi-analytical model based on Bardeen's transfer Hamiltonian approach to describe both effects. Remarkably, our model is able to reproduce several experimental observations of a metallic behavior in the contact resistance, i.e., a decreasing resistance with decreasing temperature, occurring at high gate voltage., Comment: main text + supporting information

We present a comprehensive study of the band alignments of two-dimensional (2D) semiconducting materials and highlight the possibilities of forming momentum-matched type I, II and III heterojunctions; an enticing possibility being atomic heterostructures where the constituent monolayers have band edges at the zone center. Our study, which includes the Group IV and III-V compound monolayer materials, Group V elemental monolayer materials, transition metal dichalcogenides (TMD) and transition metal trichalcogenides (TMT) reveals that almost half of these materials have conduction and/or valence band edges residing at the zone center. Using first-principles density functional calculations, we present the type of the heterojunction for 903 different possible combination of these 2D materials which establishes a periodic table of heterojunctions.

Ambipolar black phosphorus MOSFETs with record n-channel extrinsic transconductance are reported. The devices consist of multi-layer black phosphorus aligned to a local back-gate electrode with 10-nm-thick HfO2 gate dielectric. Before passivation, devices with 0.3-um gate length behaved as p-MOSFETs with peak extrinsic transconductance, gm, of 282 uS/um at VDS = -2 V. After passivation, the same devices displayed ambipolar behavior, and when tested as n-MOSFETs, had peak gm = 66 uS/um at VDS = +2 V, and similar devices on the same wafer had gm as high as 80 uS/um. These results are an important step toward realization of high-performance black phosphorus complementary logic circuits., Comment: 4 pages, 4 figures, 1 table

The operation of an integrated two-dimensional complementary metal-oxide-semiconductor inverter with well-matched input/output voltages is reported. The circuit combines a few-layer MoS2 n-MOSFET and a black phosphorus (BP) p-MOSFET fabricated using a common local backgate electrode with thin (20 nm) HfO2 gate dielectric. The constituent devices have linear threshold voltages of -0.8 V and +0.8 V and produce peak transconductances of 16 uS/um and 41 uS/um for the MoS2 n-MOSFET and BP p-MOSFET, respectively. The inverter shows a voltage gain of 3.5 at a supply voltage, VDD = 2.5 V, and has peak switching current of 108 uA and off-state current of 8.4 uA (2.4 uA) at VIN = 0 (VIN = 2.5 V). In addition, the inverter has voltage gain greater than unity for VDD > 0.5 V, has open butterfly curves for VDD > 1 V, and achieves static noise margin over 500 mV at VDD = 2.5 V. The voltage gain was found to be insensitive to temperature between 270 K and 340 K, and AC large and small-signal operation was demonstrated at frequencies up to 100 kHz. The demonstration of a complementary 2D inverter which operates in a symmetric voltage window suitable for driving a subsequent logic stage is a significant step forward in developing practical applications for devices based upon 2D materials.

Understanding the interactions of ambient molecules with graphene and adjacent dielectrics is of fundamental importance for a range of graphene-based devices, particularly sensors, where such interactions could influence the operation of the device. It is well-known that water can be trapped underneath graphene and its host substrate, however, the electrical effect of water beneath graphene and the dynamics of how it changes with different ambient conditions has not been quantified. Here, using a metal-oxide-graphene variable-capacitor (varactor) structure, we show that graphene can be used to capacitively sense the intercalation of water between graphene and HfO2 and that this process is reversible on a fast time scale. Atomic force microscopy is used to confirm the intercalation and quantify the displacement of graphene as a function of humidity. Density functional theory simulations are used to quantify the displacement of graphene induced by intercalated water and also explain the observed Dirac point shifts as being due to the combined effect of water and oxygen on the carrier concentration in the graphene. Finally, molecular dynamics simulations indicate that a likely mechanism for the intercalation involves adsorption and lateral diffusion of water molecules beneath the graphene., Comment: E.J.O. and R.M. made an equal contribution to this work

We report record performance for black phosphorus p-MOSFETs. The devices have locally patterned back gates and 20-nm-thick HfO2 gate dielectrics. Devices with effective gate length, Leff = 1.0 um display extrinsic transconductance, gm, of 101 uS/um at a drain-to-source bias voltage, Vds = -3 V. Temperature-dependent analysis also shows that the subthreshold slope, SS, is nearly ideal, with a minimum value of SS = 66 mV/decade at room temperature and Vds = -0.1 V. Furthermore, devices with 7-nm HfO2 dielectrics and Leff = 0.3 um displayed gm as high as 204 uS/um at Vds = -1.5 V.

Layered two-dimensional materials have shown novel optoelectronic properties and are well suited to be integrated in planar photonic circuits. For example, graphene has been utilized for wideband photodetection. Because graphene lacks a band gap, however, graphene photodetectors suffer from very high dark current. In contrast, layered black phosphorous, the latest addition to the family of 2D materials, is well-suited for photodetector applications due to its narrow but finite band gap. Here, we demonstrate a gated multilayer black phosphorus photodetector integrated on a silicon photonic waveguide operating in the near-infrared telecom band. In a significant advantage over graphene devices, black phosphorus photodetectors can operate under a bias with very low dark current and attain intrinsic responsivity up to 135 mA/W and 657 mA/W in 11.5nm and 100 nm thick devices, respectively, at room temperature. The photocurrent is dominated by the photovoltaic effect with a high response bandwidth exceeding 3 GHz., Comment: 15 pages, 4 figures

For optical communication, information is converted between optical and electrical signal domains at a high rate. The devices to achieve such a conversion are various types of electro-optical modulators and photodetectors. These two types of optoelectronic devices, equally important, require different materials and consequently it has been challenging to realize both using a single material combination, especially in a way that can be integrated on the ubiquitous silicon platform. Graphene, with its gapless band structure, stands out as a unique optoelectronic material that allows both photodetection and optical modulation. Here, we demonstrate a single graphene-based device that simultaneously provides both efficient optical modulation and photodetection. The graphene device is integrated on a silicon waveguide and is tunable with a gate made from another layer of graphene to achieve near-infrared photodetection responsivity of 57 mA/W and modulation depth of 64%. This novel multifunctional device may lead to many unprecedented optoelectronic applications., Comment: 18 pages, 4 figures

A wireless vapor sensor based upon the quantum capacitance effect in graphene is demonstrated. The sensor consists of a metal-oxide-graphene variable capacitor (varactor) coupled to an inductor, creating a resonant oscillator circuit. The resonant frequency is found to shift in proportion to water vapor concentration for relative humidity (RH) values ranging from 1% to 97% with a linear frequency shift of 5.7 +- 0.3 kHz / RH%. The capacitance values extracted from the wireless measurements agree with those determined from capacitance-voltage measurements, providing strong evidence that the sensing arises from the variable quantum capacitance in graphene. These results represent a new sensor transduction mechanism and pave the way for graphene quantum capacitance sensors to be studied for a wide range of chemical and biological sensing applications., Comment: 8 pages, 7 figures

To fully utilize graphene's remarkable optical properties for optoelectronic applications, it needs to be integrated in planar photonic systems. Here, we demonstrate integration of graphene on silicon photonic circuits and precise measurement of the optical absorption coefficient in a graphene/waveguide hybrid structure. A method based on Mach-Zehnder interferometry is employed to achieve high measurement precision and consistency, yielding a maximal value of absorption coefficient of 0.2 dB/{\mu}m when graphene is located directly on top of the waveguide. The results agree with theoretical model utilizing the universal ac conductivity in graphene. Our work provides an important guide for the design and optimization of integrated graphene optoelectronic devices., Comment: 14 pages, 4 figures

An electro-absorption optical modulator concept based upon a dual-graphene layer is presented. The device consists of a silicon-on-insulator waveguide upon which two graphene layers reside, separated by a thin insulating region. The lower graphene acts as a tunable absorber, while the upper layer functions as a transparent gate electrode. Calculations based upon realistic graphene material properties show that 3-dB bandwidths over 100 GHz (30 GHz) are possible at near- (\lambda=1.55 \mu m) and mid- (\lambda=3.5\mu m) infrared bands. The effect of background doping and potential fluctuations on the bandwidth, modulation depth and insertion loss are also quantified., Comment: 18 pages, 4 figure

Tunneling field-effect transistors (TFETs) have gained a great deal of recent interest due to their potential to reduce power dissipation in integrated circuits. One major challenge for TFETs so far has been achieving high drive currents, which is a prerequisite for high-performance operation. In this paper we explore the performance potential of a 1D TFET with a broken-gap heterojunction source injector using dissipative quantum transport simulations based on the nonequilibrium Green's function formalism, and the carbon nanotube bandstructure as the model 1D material system. We provide detailed insights into broken-gap TFET (BG-TFET) operation, and show that it can indeed produce less than 60mV/decade subthreshold swing at room temperature even in the presence of electron-phonon scattering. The 1D geometry is recognized to be uniquely favorable due to its superior electrostatic control, reduced carrier thermalization rate, and beneficial quantum confinement effects that reduce the off-state leakage below the thermionic limit. Because of higher source injection compared to staggered-gap and homojunction geometries, BG-TFET delivers superior performance that is comparable to MOSFET's. BG-TFET even exceeds the MOSFET performance at lower supply voltages (VDD), showing promise for low-power/high-performance applications., Comment: 34 pages, 11 figures

A general solution for the electrostatic potential in an atomic-thin-body (ATB) field-effect transistor geometry is presented. The effective electrostatic scaling length, {\lambda}eff, is extracted from the analytical model, which cannot be approximated by the lowest order eigenmode as traditionally done in SOI-MOSFETs. An empirical equation for the scaling length that depends on the geometry parameters is proposed. It is shown that even for a thick SiO2 back oxide {\lambda}eff can be improved efficiently by thinner top oxide thickness, and to some extent, with high-k dielectrics. The model is then applied to self-consistent simulation of graphene nanoribbon (GNR) Schottky-barrier field-effect transistors (SB-FETs) at the ballistic limit. In the case of GNR SB-FETs, for large {\lambda}eff, the scaling is limited by the conventional electrostatic short channel effects (SCEs). On the other hand, for small {\lambda}eff, the scaling is limited by direct source-to-drain tunneling. A subthreshold swing below 100mV/dec is still possible with a sub-10nm gate length in GNR SB-FETs., Comment: 4 figures, accepted by EDL