Ferroelectric Size Effects on Statics and Dynamics of Domain Wall.

Domain walls separating differently oriented polarization regions of ferroelectric materials are known to greatly impact nanoscale materials and device functionalities. Though the understanding of size effects in ferroelectric nanostructures has progressed, the effect of thickness downsizing on domain wall scaling behavior has remained unexplored. Using piezoresponse force microscopy, epitaxial BaTiO3 film thickness size (2-90 nm) effects on the critical scaling universality of the domain wall dynamical creep and static roughness exponents including dimensionality is demonstrated. Independently estimated static roughness exponents ranging between 0.34 and 0.28 and dynamical creep exponents transition from 0.54 to 0.22 elucidate the domain wall dimensionality transition from two- to quasi-one-dimension in the thickness range of 10-25 nm, which is later validated by evaluating effective dimensionality within the paradigm of random-bond universality. The observed interdimensional transition is further credenced to the compressive strain and long-range strain-dipolar interactions, as revealed by the structural analyses and additional measurements with modified substrate-induced strain. These results provide new insights into the understanding of size effects in nanoscale ferroelectricity, paving the way toward future nanodevices.

Artificial homeostatic temperature regulation via bio-inspired feedback mechanisms.

Homeostasis comprises one of the main features of living organisms that enables their robust functioning by adapting to environmental changes. In particular, thermoregulation, as an instance of homeostatic behavior, allows mammals to maintain stable internal temperature with tightly controlled self-regulation independent of external temperatures. This is made by a proper reaction of the thermoeffectors (like skin blood vessels, brown adipose tissue (BAT), etc.) on a wide range of temperature perturbations that reflect themselves in the thermosensitive neurons' activity. This activity is being delivered to the respective actuation points and translated into thermoeffectors' actions, which bring the temperature of the organism to the desired level, called a set-point. However, it is still an open question whether these mechanisms can be implemented in an analog electronic device: both on a system theoretical and a hardware level. In this paper, we transfer this control loop into a real electric circuit by designing an analog electronic device for temperature regulation that works following bio-inspired principles. In particular, we construct a simplified single-effector regulation system and show how spiking trains of thermosensitive artificial neurons can be processed to realize an efficient feedback mechanism for the stabilization of the a priori unknown but system-inherent set-point. We also demonstrate that particular values of the set-point and its stability properties result from the interplay between the feedback control gain and activity patterns of thermosensitive artificial neurons, for which, on the one hand, the neuronal interconnections are generally not necessary. On the other hand, we show that such connections can be beneficial for the set-point regulation and hypothesize that the synaptic plasticity in real thermosensitive neuronal ensembles can play a role of an additional control layer empowering the robustness of thermoregulation. The electronic realization of temperature regulation proposed in this paper might be of interest for neuromorphic circuits which are bioinspired by taking the basal principle of homeostasis on board. In this way, a fundamental building block of life would be transferred to electronics and become a milestone for the future of neuromorphic engineering.

A Comparative Study of Pt/Al0.72Sc0.28N/Pt-Based Thin-Film Metal-Ferroelectric-Metal Capacitors on GaN and Si Substrates.

AlxSc1-xN is a nitride-ferroelectric compatible with both CMOS and GaN technology. The origin of ferroelectricity in these ternary nitrides relies on the full inversion of nitrogen atom positions, which is a significantly different structural mechanism than conventional perovskites. Therefore, its ferroelectric characteristics exhibit a high remanent polarization and a tunable coercive field but suffer heavily from leakage currents during the switching event. In this article, we studied epitaxially grown Al0.72Sc0.28N thin films on epitaxial Pt electrode layers deposited on GaN/Al2O3 substrates. The results are compared both structurally and electrically with similar systems on SiO2/Si substrates. Our X-ray diffraction analysis showed that Al0.72Sc0.28N/epi-Pt/GaN is always a complete epitaxial stack without any significant strain gradient. Electrically, this system has an overall lower leakage current and coercive field compared to directly grown, highly crystalline, strained epitaxial Al0.72Sc0.28N/GaN, despite having a lower crystalline quality of the ferroelectric layer. In addition, decreasing the epi-Pt thickness from 100 to 10 nm resulted in further improvement of the leakage profile, which we attribute to a decrease in surface roughness in the thinner Pt. In contrast, the dominant factor of leakage in a fiber-textured system on Si substrates is the Pt(111) texture. Finally, with the combination of in-plane X-ray diffraction and high-resolution scanning transmission electron microscopy, we have demonstrated an all-epitaxial 20 nm Al0.72Sc0.28N/Pt/GaN MFM stack with a sharp interface thickness of less than 1 nm.

In-Grain Ferroelectric Switching in Sub-5 nm Thin Al0.74 Sc0.26 N Films at 1 V.

Analog switching in ferroelectric devices promises neuromorphic computing with the highest energy efficiency if limited device scalability can be overcome. To contribute to a solution, one reports on the ferroelectric switching characteristics of sub-5 nm thin Al0.74 Sc0.26 N films grown on Pt/Ti/SiO2 /Si and epitaxial Pt/GaN/sapphire templates by sputter-deposition. In this context, the study focuses on the following major achievements compared to previously available wurtzite-type ferroelectrics: 1) Record low switching voltages down to 1 V are achieved, which is in a range that can be supplied by standard on-chip voltage sources. 2) Compared to the previously investigated deposition of ultrathin Al1-x Scx N films on epitaxial templates, a significantly larger coercive field (Ec ) to breakdown field ratio is observed for Al0.74 Sc0.26 N films grown on silicon substrates, the technologically most relevant substrate-type. 3) The formation of true ferroelectric domains in wurtzite-type materials is for the first time demonstrated on the atomic scale by scanning transmission electron microscopy (STEM) investigations of a sub-5 nm thin partially switched film. The direct observation of inversion domain boundaries (IDB) within single nm-sized grains supports the theory of a gradual domain-wall driven switching process in wurtzite-type ferroelectrics. Ultimately, this should enable the analog switching necessary for mimicking neuromorphic concepts also in highly scaled devices.

Thermal Stability of the Ferroelectric Properties in 100 nm-Thick Al0.72Sc0.28N.

The discovery of ferroelectricity in aluminum scandium nitride (Al1-xScxN) opens technological perspectives for harsh environments and space-related memory applications, considering the high-temperature stability of piezoelectricity in aluminum nitride. The ferroelectric and material properties of 100 nm-thick Al0.72Sc0.28N are studied up to 873 K, combining both electrical and in situ X-ray diffraction measurements as well as transmission electron microscopy and energy-dispersive X-ray spectroscopy. The present work demonstrates that Al0.72Sc0.28N can achieve high switching polarization and tunable coercive fields in a 375 K temperature range from room temperature up to 673 K. The degradation of the ferroelectric properties in the capacitors is observed above this temperature. Reduction of the effective top electrode area and consequent oxidation of the Al0.72Sc0.28N film are mainly responsible for this degradation. A slight variation of the Sc concentration is quantified across grain boundaries, even though its impact on the ferroelectric properties cannot be isolated from those brought by the top electrode deterioration and Al0.72Sc0.28N oxidation. The Curie temperature of Al0.72Sc0.28N is confirmed to be above 873 K, thus corroborating the promising thermal stability of this ferroelectric material. The present results further support the future adoption of Al1-xScxN in memory technologies for harsh environments like applications in space missions.

Structural plasticity driven by task performance leads to criticality signatures in neuromorphic oscillator networks.

Oscillator networks rapidly become one of the promising vehicles for energy-efficient computing due to their intrinsic parallelism of execution. The criticality property of the oscillator-based networks is regarded to be essential for performing complex tasks. There are numerous bio-inspired synaptic and structural plasticity mechanisms available, especially for spiking neural networks, which can drive the network towards the criticality. However, there is no solid connection between these self-adaption mechanisms and the task performance, and it is not clear how and why particular self-adaptation mechanisms contribute to the solution of the task, although their relation to criticality is understood. Here we propose an evolutionary approach for the structural plasticity that relies solely on the task performance and does not contain any task-independent adaptation mechanisms, which usually contribute towards the criticality of the network. As a driver for the structural plasticity, we use a direct binary search guided by the performance of the classification task that can be interpreted as an interaction of the network with the environment. Remarkably, such interaction with the environment brings the network to criticality, although this property was not a part of the objectives of the employed structural plasticity mechanism. This observation confirms a duality of criticality and task performance, and legitimizes internal activity-dependent plasticity mechanisms from the viewpoint of evolution as mechanisms contributing to the task performance, but following the dual route. Finally, we analyze the trained network against task-independent information-theoretic measures and identify the interconnection graph's entropy to be an essential ingredient for the classification task performance and network's criticality.

Microstructure effects on the phase transition behavior of a prototypical quantum material.

Materials with insulator-metal transitions promise advanced functionalities for future information technology. Patterning on the microscale is key for miniaturized functional devices, but material properties may vary spatially across microstructures. Characterization of these miniaturized devices requires electronic structure probes with sufficient spatial resolution to understand the influence of structure size and shape on functional properties. The present study demonstrates the use of imaging soft X-ray absorption spectroscopy with a spatial resolution better than 2 [Formula: see text]m to study the insulator-metal transition in vanadium dioxide thin-film microstructures. This novel technique reveals that the transition temperature for the conversion from insulating to metallic vanadium dioxide is lowered by 1.2 K ± 0.4 K close to the structure edges compared to the center. Facilitated strain release during the phase transition is discussed as origin of the observed behavior. The experimental approach enables a detailed understanding of how the electronic properties of quantum materials depend on their patterning at the micrometer scale.

A spiking and adapting tactile sensor for neuromorphic applications.

The ongoing research on and development of increasingly intelligent artificial systems propels the need for bio inspired pressure sensitive spiking circuits. Here we present an adapting and spiking tactile sensor, based on a neuronal model and a piezoelectric field-effect transistor (PiezoFET). The piezoelectric sensor device consists of a metal-oxide semiconductor field-effect transistor comprising a piezoelectric aluminium-scandium-nitride (AlxSc1-xN) layer inside of the gate stack. The so augmented device is sensitive to mechanical stress. In combination with an analogue circuit, this sensor unit is capable of encoding the mechanical quantity into a series of spikes with an ongoing adaptation of the output frequency. This allows for a broad application in the context of robotic and neuromorphic systems, since it enables said systems to receive information from the surrounding environment and provide encoded spike trains for neuromorphic hardware. We present numerical and experimental results on this spiking and adapting tactile sensor.

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices.

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD.

Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices.

Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells.

Unsupervised Hebbian learning experimentally realized with analogue memristive crossbar arrays.

Conventional transistor electronics are reaching their limits in terms of scalability, power dissipation, and the underlying Boolean system architecture. To overcome this obstacle neuromorphic analogue systems are recently highly investigated. Particularly, the use of memristive devices in VLSI analogue concepts provides a promising pathway to realize novel bio-inspired computing architectures, which are able to unravel the foreseen difficulties of traditional electronics. Currently, a variety of materials and device structures are being studied along with novel computing schemes to make use of the attractive features of memristive devices for neuromorphic computing. However, a number of obstacles still have to be overcome to cast memristive devices into hardware systems. Most important is a physical implementation of memristive devices, which can cope with the high complexity of neural networks. This includes the integration of analogue and electroforming-free memristive devices into crossbar structures with no additional electronic components, such as selector devices. Here, an unsupervised, bio-motivated Hebbian based learning platform for visual pattern recognition is presented. The heart of the system is a crossbar array (16 × 16) which consists of selector-free and forming-free (non-filamentary) memristive devices, which exhibit analogue I-V characteristics.

A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus.

Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides a broad working framework for the phenomenological modelling of cellular synaptic mechanisms. In particular, we seek to understand how close a memristive system can account for the biological realism. The basic characteristics of memristive systems, i.e. voltage and memory behavior, are used to derive a voltage-based plasticity rule. We show that this model is suitable to account for a variety of electrophysiology plasticity data. Furthermore, we incorporate the plasticity model into an all-to-all connecting network scheme. Motivated by the auto-associative CA3 network of the hippocampus, we show that the implemented network allows the discrimination and processing of mnemonic pattern information, i.e. the formation of functional bidirectional connections resulting in the formation of local receptive fields. Since the presented plasticity model can be applied to real memristive devices as well, the presented theoretical framework can support both, the design of appropriate memristive devices for neuromorphic computing and the development of complex neuromorphic networks, which account for the specific advantage of memristive devices.

Memristive stochastic plasticity enables mimicking of neural synchrony: Memristive circuit emulates an optical illusion.

The human brain is able to integrate a myriad of information in an enormous and massively parallel network of neurons that are divided into functionally specialized regions such as the visual cortex, auditory cortex, or dorsolateral prefrontal cortex. Each of these regions participates as a context-dependent, self-organized, and transient subnetwork, which is shifted by changes in attention every 0.5 to 2 s. This leads to one of the most puzzling issues in cognitive neuroscience, well known as the "binding problem." The concept of neural synchronization tries to explain the problem by encoding information using coherent states, which temporally patterns neural activity. We show that memristive devices, that is, a two-terminal variable resistor that changes its resistance depending on the previous charge flow, allow a new degree of freedom for this concept: a local memory that supports transient connectivity patterns in oscillator networks. On the basis of the probability distribution of the resistance switching process of Ag-doped titanium dioxide memristive devices, a local plasticity model is proposed, which causes an autonomous phase and frequency locking in an oscillator network. To illustrate the performance of the proposed computing paradigm, the temporal binding problem is investigated in a network of memristively coupled self-sustained van der Pol oscillators. We show evidence that the implemented network allows achievement of the transition from asynchronous to multiple synchronous states, which opens a new pathway toward the construction of cognitive electronics.

Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition.

The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al2O3/Nb x O y /Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al2O3 tunnel barrier and a 2.5 mm thick Nb x O y memristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system. The results are evaluated and modeled in the framework of the plasticity model of Ziegler et al. Based on this model, which is matched to experimental data from 84 individual devices, the network performance with regard to yield, reliability, and variability is investigated numerically. As the network model, a computing scheme for pattern recognition and unsupervised learning based on the work of Querlioz et al. (2011), Sheridan et al. (2014), Zahari et al. (2015) is employed. This is a two-layer feedforward network with a crossbar array of memristive devices, leaky integrate-and-fire output neurons including a winner-takes-all strategy, and a stochastic coding scheme for the input pattern. As input pattern, the full data set of digits from the MNIST database is used. The numerical investigation indicates that the experimentally obtained yield, reliability, and variability of the memristive cells are suitable for such a network. Furthermore, evidence is presented that their strong I-V non-linearity might avoid the need for selector devices in crossbar array structures.

1-D simulation of a novel nonvolatile resistive random access memory device.

The operation of a novel, nonvolatile memory device based on a conductive ferroelectric/semiconductor thin film multilayer stack is simulated numerically. The simulation involves the self-consistent steady-state solution of the transport equation for electrons assuming a drift-diffusion transport mechanism and the Poisson equation. Special emphasis is put on the screening of the spontaneous polarization by conduction electrons as a function of the applied voltage. Depending on the orientation of the polarization in the ferroelectric layer, a high and a low resistive state are found, giving rise to a hysteretic I-V characteristic. The switching ratio, ranging from > 50% to several orders of magnitude, is calculated as a function of the dopant content. The suggested model provides one possible physical explanation of the I-V hysteresis observed for single-layer ferroelectric devices, if interfacial layers are taken into consideration. The approach will allow one to develop guidelines to improve the performance of these devices.

The role of ion transport phenomena in memristive double barrier devices.

In this work we report on the role of ion transport for the dynamic behavior of a double barrier quantum mechanical Al/Al2O3/NbxOy/Au memristive device based on numerical simulations in conjunction with experimental measurements. The device consists of an ultra-thin NbxOy solid state electrolyte between an Al2O3 tunnel barrier and a semiconductor metal interface at an Au electrode. It is shown that the device provides a number of interesting features such as an intrinsic current compliance, a relatively long retention time, and no need for an initialization step. Therefore, it is particularly attractive for applications in highly dense random access memories or neuromorphic mixed signal circuits. However, the underlying physical mechanisms of the resistive switching are still not completely understood yet. To investigate the interplay between the current transport mechanisms and the inner atomistic device structure a lumped element circuit model is consistently coupled with 3D kinetic Monte Carlo model for the ion transport. The simulation results indicate that the drift of charged point defects within the NbxOy is the key factor for the resistive switching behavior. It is shown in detail that the diffusion of oxygen modifies the local electronic interface states resulting in a change of the interface properties.

Polarity-tunable spin transport in all-oxide multiferroic tunnel junctions.

A multiferroic tunnel junction (MFTJ) promisingly offers multinary memory states in response to electric- and magnetic-fields, referring to tunneling electroresistance (TER) and tunneling magnetoresistance (TMR), respectively. In spite of recent progress, a substantial number of questions concerning the understanding of these two intertwined phenomena still remain open, e.g. the role of microstructural/chemical asymmetry at the interfaces of the junction and the effect of an electrode material on the MFTJ properties. In this regard, we look into the multiferroic effect of all-complex-oxide MFTJ (La0.7Sr0.3MnO3/Pb(Zr0.3Ti0.7)O3/La0.7Sr0.3MnO3). The results reveal apparent TER-TMR interplay-captured by the reversible electric-field control of the TMR effect. Finally, microscopy analysis on the MFTJ revealed that the observed TER-TMR interplay is perhaps mediated by microstructural and chemical asymmetry in our nominally symmetric MFTJ.

A memristive spiking neuron with firing rate coding.

Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2) and on the chemical electromigration cell Ag/TiO2-x /Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

Memristive Hebbian plasticity model: device requirements for the emulation of Hebbian plasticity based on memristive devices.

In this work we present a phenomenological model for synaptic plasticity suitable to describe common plasticity measurements of memristive devices. We show evidence that the presented model is basically compatible with advanced biophysical plasticity models, which account for a large body of experimental data on spike-timing-depending plasticity (STDP) as an asymmetric form of Hebbian learning. The basic characteristics of our model are a saturation of the synaptic weight growth and a weight dependent learning rate. Moreover, it accounts for common resistive switching behaviors of memristive devices under voltage pulse application and allows to study essential requirements of individual memristive devices for the emulation of Hebbian plasticity in neuromorphic circuits. In this respect, memristive devices based on mixed ionic/electronic and one exclusively electronic mechanism are explored. The ionic/electronic devices consist of the layer sequence metal/isolator/metal and represent today's most popular devices. The electronic device is a MemFlash-cell which is based on a conventional floating gate transistor in a diode configuration wiring scheme exhibiting a memristive (pinched) I-V characteristic.

Giant electrode effect on tunnelling electroresistance in ferroelectric tunnel junctions.

Among recently discovered ferroelectricity-related phenomena, the tunnelling electroresistance (TER) effect in ferroelectric tunnel junctions (FTJs) has been attracting rapidly increasing attention owing to the emerging possibilities of non-volatile memory, logic and neuromorphic computing applications of these quantum nanostructures. Despite recent advances in experimental and theoretical studies of FTJs, many questions concerning their electrical behaviour still remain open. In particular, the role of ferroelectric/electrode interfaces and the separation of the ferroelectric-driven TER effect from electrochemical ('redox'-based) resistance-switching effects have to be clarified. Here we report the results of a comprehensive study of epitaxial junctions comprising BaTiO(3) barrier, La(0.7)Sr(0.3)MnO(3) bottom electrode and Au or Cu top electrodes. Our results demonstrate a giant electrode effect on the TER of these asymmetric FTJs. The revealed phenomena are attributed to the microscopic interfacial effect of ferroelectric origin, which is supported by the observation of redox-based resistance switching at much higher voltages.