Zn1-xTex Ovonic Threshold Switching Device Performance and its Correlation to Material Parameters

We have experimentally demonstrated a strong correlation between the electrical properties of Zn1-xTex Ovonic threshold switching (OTS) selector device and the material properties analysed by X-ray diffraction (XRD), spectroscopic ellipsometry, and X-ray photoelectron spectroscopy (XPS). The correlation and the key material parameters determining the device performances were investigated. By comparing the experimental data with the calculation results from various analytical models previously developed for OTS materials, the electrical properties of the device were shown to be dependent on the key material parameters; the concentration of sub-gap trap states and the bandgap energy of the OTS material. This study also experimentally demonstrated that those key parameters have determined the device performance as expected from the analytical model. The origin of the OTS phenomenon and conduction mechanism were explained both experimentally and theoretically. This leads to better understanding of the conduction mechanism of OTS devices, and an insight for process improvement to optimize device performance for selector application.11Ysciescopu

Steep Slope Field-Effect Transistors With B-Te-Based Ovonic Threshold Switch Device

In this letter, a new ovonic threshold switch (OTS) device based on simple binary Boron-Tellurium (B-Te) film is developed and implemented in series with the source region of a transistor. The newly developed B-Te-based device shows excellent characteristics such as low operating voltage, low leakage current, abrupt turn-on/off slope, fast switching speed, high endurance, and high thermal stability. Due to the great properties of the B-Te OTS device, the implemented transistor exhibits subthreshold swing less than 10 mV/dec and high on/off current ratio greater than 10(5). Moreover, we present a direction of implementing an ideal transistor based on simulation results explaining the effect of off-state resistances and threshold voltages of the OTS devices on the I-DS-V-GS characteristics of the implementer transistor.11Ysciescopu

Organic core-sheath nanowire artificial synapses with femtojoule energy consumption

Emulation of biological synapses is an important step toward construction of large-scale brain-inspired electronics. Despite remarkable progress in emulating synaptic functions, current synaptic devices still consume energy that is orders of magnitude greater than do biological synapses (similar to 10 fJ per synaptic event). Reduction of energy consumption of artificial synapses remains a difficult challenge. We report organic nanowire (ONW) synaptic transistors (STs) that emulate the important working principles of a biological synapse. The ONWs emulate the morphology of nerve fibers. With a core-sheath-structured ONW active channel and a well-confined 300-nm channel length obtained using ONW lithography, similar to 1.23 fJ per synaptic event for individual ONW was attained, which rivals that of biological synapses. The ONW STs provide a significant step toward realizing low-energy-consuming artificial intelligent electronics and open new approaches to assembling soft neuromorphic systems with nanometer feature size.1161Yscopu

Reliable Multivalued Conductance States in TaOx, Memristors through Oxygen Plasma-Assisted Electrode Deposition with in Situ-Biased Conductance State Transmission Electron Microscopy Analysis

Transition metal oxide-based memristors have widely been proposed for applications toward artificial synapses. In general, memristors have two or more electrically switchable stable resistance states that device researchers see as an analogue to the ion channels found in biological synapses. The mechanism behind resistive switching in metal oxides has been divided into electrochemical metallization models and valence change models. The stability of the resistance states in the memristor vary widely depending on: oxide material, electrode material, deposition conditions, film thickness, and programming conditions. So far, it has been extremely challenging to obtain reliable memristors with more than two stable multivalued states along with endurances greater than similar to 1000 cycles for each of those states. Using an oxygen plasma-assisted sputter deposition method of noble metal electrodes, we found that the metal-oxide interface could be deposited with substantially lower interface roughness observable at the nanometer scale. This markedly improved device reliability and function, allowing for a demonstration of memristors with four completely distinct levels from similar to 6 x 10(-6) to similar to 4 x 10(-8) S that were tested up to 10(4) cycles per level. Furthermore through a unique in situ transmission electron microscopy study, we were able to verify a redox reaction-type model to be dominant in our samples, leading to the higher degree of electrical state controllability. For solid-state synapse applications, the improvements to electrical properties will lead to simple device structures, with an overall power and area reduction of at least 1000 times when compared to SRAM.11Ysciescopu

Neuromorphic computing using non-volatile memory

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and ‘Memcomputing’. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix–vector multiplication needed for algorithms such as backpropagation in an analog yet massively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices – including phase change memory, conductive-bridging RAM, filamentary and non-filamentary RRAM, and other NVMs – have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.11Yscopu

Tunnel Barrier Engineering of Titanium Oxide for High Non-Linearity of Selector-less Resistive Random Access Memory

In this study, the effect of the oxygen profile and thickness of multiple-layers TiOx on tunnel barrier characteristics was investigated to achieve high non-linearity in low-resistance state current (I-LRS). To form the tunnel barrier in multiple-layer of TiOx, tunnel barrier engineering in terms of the thickness and oxygen profile was attempted using deposition and thermal oxidation times. It modified the defect distribution of the tunnel barrier for effective suppression of ILRS at off-state (1/2V(Read)). By inserting modified tunnel barrier in resistive random access memory, a high non-linear I-LRS was exhibited with a significantly lowered I-LRS for 1/2V(Read). (C) 2014 AIP Publishing LLC.ope

Reducing Circuit Design Complexity for Neuromorphic Machine Learning Systems Based on Non-Volatile Memory Arrays

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Effect of dead layer on ferroelectric HZO films for low thermal budget

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