Couplings of Polarization with Interfacial Deep Trap and Schottky Interface Controlled Ferroelectric Memristive Switching
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36 MATERIALS SCIENCE↗
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Monolithic Oxidation Enables Ultrathin Vertically Graded Tantalum Oxide for Low‐Voltage, Low‐Variability Memristive Switching
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36 MATERIALS SCIENCE↗
Influence of surface adsorption on MoS 2 memtransistor switching kinetics
Sulfur-deficient polycrystalline two-dimensional (2D) molybdenum disulfide (MoS 2 ) memtransistors exhibit gate-tunable memristive switching to implement emerging memory operations and neuromorphic computing paradigms. Grain boundaries and sulfur vacancies are critical for memristive switching; however, the underlying physical mechanisms are not fully understood. Furthermore, the adsorption of water and gaseous species strongly perturbs electronic transport in monolayer MoS 2 , and little work has been done to explore the influence of surface interactions on defect-related kinetics that produces memristive switching. Here, we study the switching kinetics of back-gated MoS 2 memtransistors using current transient measurements in a controlled atmosphere chamber. Here, we observe that adsorbed water molecules lead to suppression of the electronic trap-filling processes concomitant with the resistive switching process, resulting in altered kinetics of the resistive switching. Additionally, using the transient response from “bunched” drain voltage pulse trains performed as a function of temperature, we extract the energy of the affected trap state and find that it places the trap roughly midgap [E T = E C – 0.7 (±0.4) eV]. Our results highlight the importance of controlling for surface interactions that may affect switching kinetics in 2D memtransistors, synaptic transistors, and related memory devices.
36 MATERIALS SCIENCE↗
Self‐Assembled NiO Nanocrystal Arrays as Memristive Elements
Abstract Memristive switching, a nonlinear current–voltage ( I – V ) characteristic, has seen a tremendous surge in interest as an approach to achieve implementation of synaptic functions. The memristive switching behavior of self‐assembled NiO nanocrystals is investigated via scanning probe microscopy, based on first‐order reversal curve current–voltage spectroscopy. Synaptic switching is clearly observed as a direct consequence of filament growth (i.e., gradually increased conductance) in the nanocrystals. A spatial dependency of the conduction in the nanocrystals suggests that there is a localization of the switching filament. The current understanding of this localization ignores features related to local lateral variation current, which can generate an excessive local heat and temperature such as electrical dissipation. The observation of low electrical dissipation at the edge of the nanocrystals shows that less energy is wasted as heat such that the bias applied can be utilized more efficiently to assist the nucleation of the filament and thus reduces the power consumption. Electrical power dissipation is also found to scale with nanocrystal height and has spatial dependence within the nanocrystals. The combination of synaptic switching and high density of the nanocrystals demonstrate that it is feasible to exploit them to create a basic architecture for neuromorphic memory devices.
Kurnia, Fran↗
Atomic-scale oxygen-vacancy engineering in Sub-2 nm thin Al 2 O 3 /MgO memristors
Ultrathin (sub-2 nm) Al 2 O 3 /MgO memristors were recently developed using an in vacuo atomic layer deposition (ALD) process that minimizes unintended defects and prevents undesirable leakage current. These memristors provide a unique platform that allows oxygen vacancies (V O ) to be inserted into the memristor with atomic precision and study how this affects the formation and rupture of conductive filaments (CFs) during memristive switching. Herein, we present a systematic study on three sets of ultrathin Al 2 O 3 /MgO memristors with V O -doping via modular MgO atomic layer insertion into an otherwise pristine insulating Al 2 O 3 atomic layer stack (ALS) using an in vacuo ALD. At a fixed memristor thickness of 17 Al 2 O 3 /MgO atomic layers (~1.9 nm), the properties of the memristors were found to be affected by the number and stacking pattern of the MgO atomic layers in the Al 2 O 3 /MgO ALS. Importantly, the trend of reduced low-state resistance and the increasing appearance of multi-step switches with an increasing number of MgO atomic layers suggests a direct correlation between the dimension and dynamic evolution of the conducting filaments and the V O concentration and distribution. Understanding such a correlation is critical to an atomic-scale control of the switching behavior of ultrathin memristors.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
In Situ Study of Resistive Switching in a Nitride‐Based Memristive Device
Abstract Resistive switching (RS) devices with ultra‐low‐voltage threshold and reliable switching repeatability exhibits great potential applications in energy‐efficient data storage and neuromorphic computing. Understanding switching mechanisms at nanoscale is critical to design RS devices with improved performance. In this work, a lamella memristive device using focused ion beam (FIB) method based on the metal/TiO x /TiN/Si structure device is fabricated. In situ transmission electron microscopy (TEM) and current–voltage ( I–V ) characteristic demonstrate that the lamella device shows a volatile RS behavior with a threshold switching at ≈ ± 0.4 V. In situ scanning transmission electron microscopy (STEM) experiments with electron energy loss spectroscopy (EELS) reveal that the charge carriers such as oxygen vacancies migrate under positive/negative DC bias and modulate Schottky barriers at the top and bottom metal/semiconductor interfaces. The RS mechanism of the lamella device is based on the Schottky barriers modulation and Joule heating assisted electric field triggered thermal runaway (FTTR) occurred at the metal/semiconductor interfaces. The fundamental insights gained from this study presents a perspective on interface‐type RS devices processing and opens up new technological opportunities of fabricating ultra‐low‐energy memristive devices.
36 MATERIALS SCIENCE↗
In-situ TEM EELS analysis of memristive thin films for neuromorphic computing
Neuromorphic computing stands as a promising frontier for advancing AI algorithms and applications like ChatGBT, offering significant energy efficiency gains. This paper delves into the hardware design intricacies of memristive thin films and their elementary switching mechanisms, including anion migration, electron migration, and phase transitions. Through comprehensive analysis of electron energy loss spectroscopy (EELS) data via in-situ transmission electron microscopy (TEM), we will deduce the primary memristive switching mechanisms vital for optimizing thin film fabrication parameters and achieving desired film thickness, conductivity, and memory retention. A single crystal ptype Si substrate was used with TiN as the bottom metal electrode, TiO x as the insulating dielectric layer, and Pt as the top metal electrode. In-situ TEM was able to tell us the thin film didn’t behave like a filamentary or phase transition material. EELS data deduced that electron trapping/detrapping was one of the primary switching mechanisms. By shedding light on these elementary mechanisms, our study aims to catalyze the development of more 2 efficient and effective neuromorphic computing systems to be deployed into mainstream technologies.
97 MATHEMATICS AND COMPUTING↗
Phase Transition in a Memristive Suspended MoS 2 Monolayer Probed by Opto- and Electro-Mechanics
Semiconducting monolayers of a 2D material are able to concatenate multiple interesting properties into a single component. Here, by combining opto-mechanical and electronic measurements, we demonstrate the presence of a partial 2H–1T' phase transition in a suspended 2D monolayer membrane of MoS 2 . Electronic transport shows unexpected memristive properties in the MoS 2 membrane, in the absence of any external dopants. A strong mechanical softening of the membrane is measured concurrently and may only be related to the 2H–1T' phase transition, which imposes a 3% directional elongation of the topological 1T' phase with respect to the semiconducting 2H. We note that only a few percent 2H–1T' phase switching is sufficient to observe measurable memristive effects. Our experimental results combined with first-principles total energy calculations indicate that sulfur vacancy diffusion plays a key role in the initial nucleation of the phase transition. Our study clearly shows that nanomechanics represents an ultrasensitive technique to probe the crystal phase transition in 2D materials or thin membranes. Lastly, a better control of the microscopic mechanisms responsible for the observed memristive effect in MoS 2 is important for the implementation of future devices.
2D materials↗
Ultra-Fast Non-Volatile Resistive Switching Devices with Over 512 Distinct and Stable Levels for Memory and Neuromorphic Computing
Low-current multilevel programmability with inherent non-volatility and high stability of resistance states is required for both multi-bit memory storage and deep learning accelerators but is difficult to achieve. Here, in a resistive switching system, this work realizes >512 (>9 bits) distinct non-volatile conductance levels with stable retention for each state with current levels down to the nanoampere range, highly promising for potential integration with small processing nodes with ultra-low power consumption requirements. This is achieved by demonstrating a new thin film design concept that encompasses three key features: an ultra-thin epitaxial oxygen ionic switching layer that provides a tunable energy barrier at the bottom electrode, an overcoat amorphous layer that acts as an ion migration barrier for stable state retention, and a partial conductive filament as a localized electronic transport channel to the epitaxial switching layer. A large dynamic resistance range of up to seven orders of magnitude is achieved with reset-free transitions among intermediate states, and programmability is demonstrated with ultra-fast (20 ns) pulses. Artificial neural network (ANN) simulations, based on the experimental performance and its non-idealities, demonstrate close-to-ideal inference accuracies for various Modified National Institute of Standards and Technology (MNIST) data sets.
36 MATERIALS SCIENCE↗
Imaging of Electrothermal Filament Formation in a Mott Insulator
Resistive switching—the current- and voltage-induced change of electrical resistance—is at the core of memristive devices, which play an essential role in the emerging field of neuromorphic computing. This study is about resistive switching in a Mott insulator, which undergoes a thermally driven metal-to-insulator transition. Two distinct switching mechanisms are reported for such a system: electric-field-driven resistive switching and electrothermal resistive switching. The latter results from an instability caused by Joule heating. Here, we present the visualization of the reversible resistive switching in a planar V 2 O 3 thin-film device using high-resolution wide-field microscopy in combination with electric transport measurements. We investigate the interaction of the electrothermal instability with the strain-induced spontaneous phase separation in the V 2 O 3 thin film at the Mott transition. The photomicrographs show the formation of a narrow metallic filament with a minimum width ≲500 nm. Although the filament formation and the overall shape of the current-voltage characteristics (IVCs) are typical of an electrothermal breakdown, we also observe atypical effects such as oblique filaments, filament splitting, and hysteretic IVCs with sawtoothlike jumps at high currents in the low-resistance regime. We are able to reproduce the experimental results in a numerical model based on a two-dimensional resistor network. This model demonstrates that resistive switching in this case is indeed electrothermal and that the intrinsic heterogeneity is responsible for the atypical effects. This heterogeneity is strongly influenced by strain, thereby establishing a link between switching dynamics and structural properties.
42 ENGINEERING↗
Electrochemical Doping of Halide Perovskites by Noble Metal Interstitial Cations
Abstract Metal halide perovskites are an attractive class of semiconductors, but it has proven difficult to control their electronic doping by conventional strategies due to screening and compensation by mobile ions or ionic defects. Noble‐metal interstitials represent an under‐studied class of extrinsic defects that plausibly influence many perovskite‐based devices. In this work, doping of metal halide perovskites is studied by electrochemically formed Au + interstitial ions, combining experimental data on devices with a computational analysis of Au + interstitial defects based on density functional theory (DFT). Analysis suggests that Au + cations can be easily formed and migrate through the perovskite bulk via the same sites as iodine interstitials (I i + ). However, whereas I i + compensates n‐type doping by electron capture, the noble‐metal interstitials act as quasi‐stable n‐dopants. Experimentally, voltage‐dependent, dynamic doping by current density–time ( J–t ), electrochemical impedance, and photoluminescence measurements are characterized. These results provide deeper insight into the potential beneficial and detrimental impacts of metal electrode reactions on long‐term performance of perovskite photovoltaic and light‐emitting diodes, as well as offer an alternative doping explanation for the valence switching mechanism of halide‐perovskite‐based neuromorphic and memristive devices.
14 SOLAR ENERGY↗
Nanoscale Imaging and Control of Volatile and Non-Volatile Resistive Switching in VO 2
Control of the metal-insulator phase transition is vital for emerging neuromorphic and memristive technologies. The ability to alter the electrically driven transition between volatile and non-volatile states is particularly important for quantum-materials-based emulation of neurons and synapses. The major challenge of this implementation is to understand and control the nanoscale mechanisms behind these two fundamental switching modalities. In this study, in situ X-ray nanoimaging is used to follow the evolution of the nanostructure and disorder in the archetypal Mott insulator VO 2 during an electrically driven transition. Our findings demonstrate selective and reversible stabilization of either the insulating or metallic phases achieved by manipulating the defect concentration. This mechanism enables us to alter the local switching response between volatile and persistent regimes and demonstrates a new possibility for nanoscale control of the resistive switching in Mott materials.
36 MATERIALS SCIENCE↗
Polarization-controlled volatile ferroelectric and capacitive switching in Sn 2 P 2 S 6
Abstract Smart electronic circuits that support neuromorphic computing on the hardware level necessitate materials with memristive, memcapacitive, and neuromorphic- like functional properties; in short, the electronic response must depend on the voltage history, thus enabling learning algorithms. Here we demonstrate volatile ferroelectric switching of Sn 2 P 2 S 6 at room temperature and see that initial polarization orientation strongly determines the properties of polarization switching. In particular, polarization switching hysteresis is strongly imprinted by the original polarization state, shifting the regions of non-linearity toward zero-bias. As a corollary, polarization switching also enables effective capacitive switching, approaching the sought-after regime of memcapacitance. Landau–Ginzburg–Devonshire simulations demonstrate that one mechanism by which polarization can control the shape of the hysteresis loop is the existence of charged domain walls (DWs) decorating the periphery of the repolarization nucleus. These walls oppose the growth of the switched domain and favor back-switching, thus creating a scenario of controlled volatile ferroelectric switching. Although the measurements were carried out with single crystals, prospectively volatile polarization switching can be tuned by tailoring sample thickness, DW mobility and electric fields, paving way to non-linear dielectric properties for smart electronic circuits.
97 MATHEMATICS AND COMPUTING↗
SwitchX : Gmin-Gmax Switching for Energy-efficient and Robust Implementation of Binarized Neural Networks on ReRAM Xbars
Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of the synapses. We propose SwitchX mapping of BNN weights onto ReRAM crossbars such that the impact of crossbar non-idealities, that lead to degradation in computational accuracy, are minimized. Essentially, SwitchX maps the binary weights in such a manner that a crossbar instance comprises of more HRS than LRS synapses. We find BNNs mapped onto crossbars with SwitchX to exhibit better robustness against adversarial attacks than the standard crossbar mapped BNNs, the baseline. Finally, we combine SwitchX with state-aware training (that further increases the feasibility of HRS states during weight mapping) to boost the robustness of a BNN on hardware. We find that this approach yields stronger defense against adversarial attacks than adversarial training, a state-of the-art software defense. We perform experiments on a VGG16 BNN with benchmark datasets (CIFAR-10, CIFAR-100 and TinyImagenet) and use Fast Gradient Sign Method (ϵ = 0.05 to 0.3) and Projected Gradient Descent (ϵ = $\frac{2}{255}$ to $\frac{32}{255}$, α = $\frac{2}{255}$) adversarial attacks. We show that SwitchX combined with state-aware training can yield upto ~35% improvements in clean accuracy and ~6–16% in adversarial accuracies against conventional BNNs. Furthermore, an important by-product of SwitchX mapping is increased crossbar power savings, owing to an increased proportion of HRS synapses, which is furthered with state-aware training. We obtain upto ~21–22% savings in crossbar power consumption for state-aware trained BNN mapped via SwitchX on 16 × 16 and 32 × 32 crossbars using the CIFAR-10 and CIFAR-100 datasets.
97 MATHEMATICS AND COMPUTING↗
In operando cryo-STEM of pulse-induced charge density wave switching in TaS 2
The charge density wave material 1T-TaS 2 exhibits a pulse-induced insulator-to-metal transition, which shows promise for next-generation electronics such as memristive memory and neuromorphic hardware. However, the rational design of TaS 2 devices is hindered by a poor understanding of the switching mechanism, the pulse-induced phase, and the influence of material defects. Here, we operate a 2-terminal TaS 2 device within a scanning transmission electron microscope at cryogenic temperature, and directly visualize the changing charge density wave structure with nanoscale spatial resolution and down to 300 μs temporal resolution. We show that the pulse-induced transition is driven by Joule heating, and that the pulse-induced state corresponds to the nearly commensurate and incommensurate charge density wave phases, depending on the applied voltage amplitude. With our in operando cryogenic electron microscopy experiments, we directly correlate the charge density wave structure with the device resistance, and show that dislocations significantly impact device performance. This work resolves fundamental questions of resistive switching in TaS 2 devices, critical for engineering reliable and scalable TaS 2 electronics.
36 MATERIALS SCIENCE↗
Quantum graph models for transport in filamentary switching
The formation of metallic nanofilaments bridging two electrodes across an insulator is a mechanism for resistive switching. Examples of such phenomena include atomic synapses, which constitute a distinct class of memristive devices the behavior of which is closely tied to the properties of the filament. Until recently, experimental investigation of the low-temperature regime and quantum transport effects has been limited. However, with growing interest in understanding the true impacts of the filament on device conductance, comprehending quantum effects has become crucial for quantum neuromorphic hardware. Here, we discuss quantum transport resulting from filamentary switching in a narrow region where the continuous approximation of the contact is not valid, and only a few atoms are involved. In this scenario, the filament can be represented by a graph depicting the adjacency of atoms and the overlap between atomic orbitals. Using the theory of quantum graphs with locally diffusive node scattering, we calculate the scattering amplitude of charge carriers on this graph and explore the interplay between filamentary formation and quantum transport effects.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
An Interface–Type Memristive Device for Artificial Synapse and Neuromorphic Computing
Interface-type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament-type (FT) devices for neuromorphic computing because of their uniform, filament-free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface-dominated memristive device is demonstrated using a simple Au/Nb-doped SrTiO 3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface-controlled RS, the proposed device shows low device-to-device, cell-to-cell, and cycle-to-cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next-generation neuromorphic computing.
36 MATERIALS SCIENCE↗