1,720,992 research outputs found
Excellent Threshold Selector Characteristics of Cu2S-based Atomic Switch Device
No full textBipolar-threshold switching was demonstrated using volatile-atomic switches (VAS) for cross-point selector applications. The VAS (Cu/Cu2S/W) exhibits asymmetric current?voltage (I?V) characteristics. An abrupt conductance change occurs by the formation of a Cu filament when a positive voltage is applied to the Cu electrode. Meanwhile, nonlinear I?V characteristics are observed because of the high concentration of vacancy of Cu under a negative voltage. We thus introduce the complementary-VAS configuration for the self-compliance characteristics at both polarities. Furthermore, the increased the switching voltage at a fast sweeping rate allows the VAS to show a strong immunity to electrical disturbance. ? 2017 The Electrochemical Society. All rights reserved.112sciescopu
Excellent Threshold Selector Characteristics of Cu2S-based Atomic Switch Device
No full textBipolar-threshold switching was demonstrated using volatile-atomic switches (VAS) for cross-point selector applications. The VAS (Cu/Cu2S/W) exhibits asymmetric current–voltage (I–V) characteristics. An abrupt conductance change occurs by the formation of a Cu filament when a positive voltage is applied to the Cu electrode. Meanwhile, nonlinear I–V characteristics are observed because of the high concentration of vacancy of Cu under a negative voltage. We thus introduce the complementary-VAS configuration for the self-compliance characteristics at both polarities. Furthermore, the increased the switching voltage at a fast sweeping rate allows the VAS to show a strong immunity to electrical disturbance.
Improved On-Current of Ag/NiO/TiO2/Pt Threshold Switching Device by Suppressing the Ag Incorporation
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CMOS compatible low-power volatile atomic switch for steep-slope FET devices
No full textIn this paper, we demonstrate a volatile atomic switch that can be utilized for obtaining steep subthreshold swing (SS) (60 mV/dec). The result shows an improvement in the SS, which results from the transition of the atomic switch between the ON and OFF states, which is caused by the formation and rupture of a conductive filament. As a result, excellent switching characteristics are obtained for the FETs, such as low I-OFF (similar to 10(-5) mu A/mu m), high I-NO/I-OFF ratio (similar to 10(-5)), low V-DD (similar to 0.25 V), and steep SS (<5 mV/dec). Published by AIP Publishing.11Nsciescopu
Thermally Stable Resistive Switching Characteristics of Te-Based Conductive-Bridge Memoryby Optimizing Zr-Te Composition
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Proton–Based Three–Terminal Synapse Device with Analog Conductance Change and Linear I-V Characteristics for Neuromorphic Systems
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Hardware Implementation of Neural Network Using Pre-Programmed Resistive Device for Pattern Recognition
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Controllable quantized conductance for multilevel data storage applications using conductive bridge random access memory
No full textIn this paper, we investigate the quantized conduction behavior of conductive bridge random access memory (CBRAM) with varied materials and ramping rates. We report stable and reproducible quantized conductance states with integer multiples of fundamental conductance obtained by optimizing the voltage ramping rate and the Ti-diffusion barrier (DB) at the Cu/HfO2 interface. Owing to controlled diffusion of Cu ions by the Ti-DB and the optimized ramping rate, through which it was possible to control the time delay of Cu ion reduction, more than seven levels of discrete conductance states were clearly observed. Analytical modeling was performed to determine the rate-limiting step in filament growth based on an electrochemical redox reaction. Our understanding of the fundamental mechanisms of quantized conductance behaviors provide a promising future for the multi-bit CBRAM device.112Nsciescopu
RRAM-based synapse devices for neuromorphic systems
No full textHardware artificial neural network (ANN) systems with high density synapse array devices can perform massive parallel computing for pattern recognition with low power consumption. To implement a neuromorphic system with on-chip training capability, we need to develop an ideal synapse device with various device requirements, such as scalability, MLC characteristics, low power operation, data retention, and symmetric/linear conductance changes under potentiation/depression modes. Although various devices have been proposed for synapse applications, they have limitations for application in neuromorphic systems. In this paper, we will cover various RRAM synapse devices, such as filamentary switching RRAM (HfOx, TaOx, Cu-CBRAM) and analog RRAM devices, based on interface resistive switching (Pr0.7Ca0.3MnOx and TiOx) and ferroelectric polarization (HfZrOx). By optimizing potentiation/depression conditions, we could improve the conductance linearity and MLC characteristics of filamentary synapse devices. Interface RRAM has better MLC characteristics with limited retention and conductance linearity. By controlling the reactivity of metal electrodes and the oxygen concentration in oxides, we can modulate the synapse characteristics. Metal-Ferroelectric-Insulator-Semiconductor (MFIS) FET devices exhibit good retention characteristics and analog memory characteristics due to polarization. Based on various synapse device characteristics, we have estimated the pattern recognition accuracy of MNIST handwritten digits and CIFAR-10 datasets. We have confirmed that synapse device characteristics directly affect the pattern recognition accuracy of ANNs. In order to simultaneously satisfy all the requirements of synapse devices, it is necessary to develop new technology capable of controlling the movement of oxygen vacancies and metal ions at the atomic scale. Considering the limited synapse characteristics of current 2 -terminal RRAM devices, hardware ANNs capable of only off -chip training can be constructed by optimizing the current RRAM devices by limiting the bit number. A 3 -terminal synapse device or a device based on a new operation principle should be developed as an alternative for on -chip training applications.11Nsciescopu
Proton-based Three-terminal Synapse Device with Symmetric and Analog Conductance Change Characteristics for Neuromorphic Systems
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