434 research outputs found

    Memristors for the Curious Outsiders

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    We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

    Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition

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    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

    Engineering method for tailoring electrical characteristics in TiN/TiOx/HfOx/Au Bi-layer oxide memristive devices

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    Memristive devices have led to an increased interest in neuromorphic systems. However, different device requirements are needed for the multitude of computation schemes used there. While linear and time-independent conductance modulation is required for machine learning, non-linear and time-dependent properties are necessary for neurobiologically realistic learning schemes. In this context, an adaptation of the resistance switching characteristic is necessary with regard to the desired application. Recently, bi-layer oxide memristive systems have proven to be a suitable device structure for this purpose, as they combine the possibility of a tailored memristive characteristic with low power consumption and uniformity of the device performance. However, this requires technological solutions that allow for precise adjustment of layer thicknesses, defect densities in the oxide layers, and suitable area sizes of the active part of the devices. For this purpose, we have investigated the bi-layer oxide system TiN/TiOx/HfOx/Au with respect to tailored I-V non-linearity, the number of resistance states, electroforming, and operating voltages. Therefore, a 4-inch full device wafer process was used. This process allows a systematic investigation, i.e., the variation of physical device parameters across the wafer as well as a statistical evaluation of the electrical properties with regard to the variability from device to device and from cycle to cycle. For the investigation, the thickness of the HfOx layer was varied between 2 and 8 nm, and the size of the active area of devices was changed between 100 and 2,500 µm2. Furthermore, the influence of the HfOx deposition condition was investigated, which influences the conduction mechanisms from a volume-based, filamentary to an interface-based resistive switching mechanism. Our experimental results are supported by numerical simulations that show the contribution of the HfOx film in the bi-layer memristive system and guide the development of a targeting device

    Tailored electrical characteristics in multilayer metal-oxide-based-memristive devices

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    Auf Mehrlagen-Metalloxiden basierende memristive Bauelemente sind einer der vielversprechendsten Kandidaten für neuromorphes Computing. Allerdings stellen spezifische Anwendungen des neuromorphen Computings unterschiedliche Anforderungen an die memristiven Bauelemente. Eine ungelöste Herausforderung in der technologischen Entwicklung ist daher das maßgeschneiderte Design von memristiven Bauelementen für spezifische Anwendungen. Insbesondere die unterschiedlichen Materialien des Schichtstapels erschweren die Herstellungsprozesse aufgrund einer großen Anzahl von Parametern, wie z. B. der Stapelsequenzen und -dicken und der Qualität sowie der Eigenschaften der einzelnen Schichten. Daher sind systematische Untersuchungen der einzelnen Bauelementparameter besonders entscheidend. Darüber hinaus müssen sie mit einem tiefgreifenden Verständnis der zugrundeliegenden physikalischen Prozesse kombiniert werden, um die Lücke zwischen Materialdesign und elektrischen Eigenschaften der resultierenden memristiven Bauelemente zuschließen. Um memristive Bauelemente mit unterschiedlichen resistiven Schalteigenschaften zu erhalten, werden verschiedene Abfolgen und Kombinationen von drei Metalloxidschichten (TiOx, HfOx, und AlOx) hergestellt und untersucht. Zunächst werden einschichtige Oxidbauelemente untersucht, um Kandidaten für mehrschichtige Stapel zu identifizieren. Zweitens werden zweischichtige TiOx/HfOx Oxidbauelemente hergestellt. Anhand von systematischen Experimenten und statistischen Analysen wird gezeigt, dass die Stöchiometrie, die Dicke, und die Fläche des Bauelements die Betriebsspannungen, die Nichtlinearität beim resistiven Schalten und die Variabilität beeinflussen. Drittens werden TiOx/AlOx/HfOx-basierte Bauelemente hergestellt. Durch das Hinzufügen von AlOx in die zweischichtigen Oxidstapel weisen diese dreischichtigen Bauelemente optimale elektrische Eigenschaften für den Einsatz in neuromorpher Hardware auf, wie z. B. elektroformierungsfreies und strombegrenzungsloses Schalten sowie eine lange Lebensdauer. Die entwickelten memristiven Bauelemente werden in Systeme, wie Kreuzpunkt-Strukturen und Ein-Transistor-ein-Memristor-Konfigurationen integriert. Hier wird die Eignung für effizientes neuromorphes Computing bewertet. Außerdem werden Methoden zur stufenlosen analogen Einstellung des Widerstands der Bauelemente demonstriert. Diese Eigenschaft ermöglicht effiziente neuromorphe Rechenschemata. Diese umfassende Studie beleuchtet die Beziehung zwischen den Bauelementparametern und den elektrischen Eigenschaften von mehrschichtigen memristiven Bauelementen auf Metalloxidbasis. Auf dieser Grundlage werden maßgeschneiderte Methoden für spezifische neuromorphe Anwendungen entwickelt.Multilayer metal-oxide-based-memristive devices are one of the most promising candidates for neuromorphic computing. However, specific applications of neuromorphic computing call for different requirements for memristive devices. Therefore, an open challenge in technological development is the tailored design of memristive devices for specific applications. In particular, multilayer stacks complicate fabrication processes due to a large number of device parameters such as staking sequences and thicknesses, quality, and property of each layer. Therefore, systematic investigations of the individual device parameters are particularly decisive. Moreover, they need to be combined with a profound understanding of the underlying physical processes to bridge the gap between material design and electrical characteristics of the resulting memristive devices. To obtain memristive devices with different resistance switching characteristics, various sequences and combinations of three metal oxide layers (TiOx, HfOx, and AlOx) are fabricated and studied. First, single-layer oxide devices are investigated to find desirable multilayer stacks for memristive devices. Second, TiOx/HfOx-based bilayer oxide devices are fabricated. Via systematic experiments and statistical analysis, it is shown that the stoichiometry, thickness, and device area influence operating voltages, non-linearity in resistive switching, and variability. Third, TiOx/AlOx/HfOx-based devices are fabricated. By adding AlOx into the bilayer oxide stacks, these trilayer devices present favorable electrical features for use in neuromorphic hardware, such as electroforming-free and compliance-free switching as well as long retention. The developed memristive devices are integrated into systems such as crossbar structures and one-transistor-one-memristor configurations. Here, suitability for efficient neuromorphic computing is assessed. Also, methods to tune the device resistance gradually in an analog fashion are demonstrated. This feature allows for efficient neuromorphic computation. This comprehensive study highlights the relationship between device parameters and electrical properties of multilayer metal-oxide-based memristive devices. On this basis, tailoring methodologies are established for specific neuromorphic applications

    Modulating the Filamentary-Based Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3 Layers

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    The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2 O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I–V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit

    A walk on the frontier of energy electronics with power ultra-wide bandgap oxides and ultra-thin neuromorphic 2D materials

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    Altres ajuts: the ICN2 is funded also by the CERCA programme / Generalitat de CatalunyaUltra-wide bandgap (UWBG) semiconductors and ultra-thin two-dimensional materials (2D) are at the very frontier of the electronics for energy management or energy electronics. A new generation of UWBG semiconductors will open new territories for higher power rated power electronics and deeper ultraviolet optoelectronics. Gallium oxide - GaO(4.5-4.9 eV), has recently emerged as a suitable platform for extending the limits which are set by conventional (-3 eV) WBG e.g. SiC and GaN and transparent conductive oxides (TCO) e.g. In2O3, ZnO, SnO2. Besides, GaO, the first efficient oxide semiconductor for energy electronics, is opening the door to many more semiconductor oxides (indeed, the largest family of UWBGs) to be investigated. Among these new power electronic materials, ZnGa2O4 (-5 eV) enables bipolar energy electronics, based on a spinel chemistry, for the first time. In the lower power rating end, power consumption also is also a main issue for modern computers and supercomputers. With the predicted end of the Moores law, the memory wall and the heat wall, new electronics materials and new computing paradigms are required to balance the big data (information) and energy requirements, just as the human brain does. Atomically thin 2D-materials, and the rich associated material systems (e.g. graphene (metal), MoS2 (semiconductor) and h-BN (insulator)), have also attracted a lot of attention recently for beyond-silicon neuromorphic computing with record ultra-low power consumption. Thus, energy nanoelectronics based on UWBG and 2D materials are simultaneously extending the current frontiers of electronics and addressing the issue of electricity consumption, a central theme in the actions against climate chang
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