Distinguishing the Rhombohedral Phase from Orthorhombic Phases in Epitaxial Doped HfO Ferroelectric Films.

Epitaxial strain plays an important role in the stabilization of ferroelectricity in doped hafnia thin films, which are emerging candidates for Si-compatible nanoscale devices. Here, we report on epitaxial ferroelectric thin films of doped HfO deposited on LaSrMnO-buffered SrTiO substrates, LaSrMnO SrTiO-buffered Si (100) wafers, and trigonal AlO substrates. The investigated films appear to consist of four domains in a rhombohedral phase for films deposited on LaSrMnO-buffered SrTiO substrates and two domains for those deposited on sapphire.

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 BaTiO 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.

A Comparative Study of Pt/AlScN/Pt-Based Thin-Film Metal-Ferroelectric-Metal Capacitors on GaN and Si Substrates.

AlScN 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-Grain Ferroelectric Switching in Sub-5 nm Thin Al Sc 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 Al Sc N films grown on Pt/Ti/SiO /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.

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.

Thermal Stability of the Ferroelectric Properties in 100 nm-Thick AlScN.

The discovery of ferroelectricity in aluminum scandium nitride (AlScN) 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 AlScN 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 AlScN can achieve high switching polarization and tunable coercive fields in a 375 K temperature range from room temperature up to 673 K.

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.

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.

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.

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 (AlScN) layer inside of the gate stack.

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.

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.

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.

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.

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/AlO/Nb O /Au and are fabricated on a 4-inch wafer.

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/AlO/NbO/Au memristive device based on numerical simulations in conjunction with experimental measurements. The device consists of an ultra-thin NbO solid state electrolyte between an AlO 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.

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.

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.

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.

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.

Bipolar switching polarity reversal by electrolyte layer sequence in electrochemical metallization cells with dual-layer solid electrolytes.

Bipolar switching behaviours of electrochemical metallization (ECM) cells with dual-layer solid electrolytes (SiOx-Ge0.3Se0.7) were analyzed.

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.