scholarly journals Electron-beam-irradiated rhenium disulfide memristors with low variability for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Sifan Li ◽  
Bochang Li ◽  
Xuewei Feng ◽  
Li Chen ◽  
Yesheng Li ◽  
...  
Keyword(s):  
Electron Beam ◽  
Resistive Switching ◽  
Neuromorphic Computing ◽  
Reset Voltage ◽  
Voltage Variation ◽  
Artificial Synapse ◽  
Metal Insulator Metal ◽  
Temporal And Spatial ◽  
Transition Voltage

AbstractState-of-the-art memristors are mostly formed by vertical metal–insulator–metal (MIM) structure, which rely on the formation of conductive filaments for resistive switching (RS). However, owing to the stochastic formation of filament, the set/reset voltage of vertical MIM memristors is difficult to control, which results in poor temporal and spatial switching uniformity. Here, a two-terminal lateral memristor based on electron-beam-irradiated rhenium disulfide (ReS2) is realized, which unveils a resistive switching mechanism based on Schottky barrier height (SBH) modulation. The devices exhibit a forming-free, stable gradual RS characteristic, and simultaneously achieve a small transition voltage variation during positive and negative sweeps (6.3%/5.3%). The RS is attributed to the motion of sulfur vacancies induced by voltage bias in the device, which modulates the ReS2/metal SBH. The gradual SBH modulation stabilizes the temporal variation in contrast to the abrupt RS in MIM-based memristors. Moreover, the emulation of long-term synaptic plasticity of biological synapses is demonstrated using the device, manifesting its potential as artificial synapse for energy-efficient neuromorphic computing applications.

scholarly journals ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

2021 ◽  
Vol 3 ◽  
Author(s):  
Yifu Huang ◽  
Yuqian Gu ◽  
Xiaohan Wu ◽  
Ruijing Ge ◽  
Yao-Feng Chang ◽  
...  
Keyword(s):  
Data Storage ◽  
Resistive Switching ◽  
Random Access ◽  
Long Term Potentiation ◽  
Computing System ◽  
Switching Behavior ◽  
Active Layer Thickness ◽  
Neuromorphic Computing ◽  
Multiple Step

Resistive random-access memory (RRAM) devices have drawn increasing interest for the simplicity of its structure, low power consumption and applicability to neuromorphic computing. By combining analog computing and data storage at the device level, neuromorphic computing system has the potential to meet the demand of computing power in applications such as artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). Monolayer rhenium diselenide (ReSe2), as a two-dimensional (2D) material, has been reported to exhibit non-volatile resistive switching (NVRS) behavior in RRAM devices with sub-nanometer active layer thickness. In this paper, we demonstrate stable multiple-step RESET in ReSe2 RRAM devices by applying different levels of DC electrical bias. Pulse measurement has been conducted to study the neuromorphic characteristics. Under different height of stimuli, the ReSe2 RRAM devices have been found to switch to different resistance states, which shows the potentiation of synaptic applications. Long-term potentiation (LTP) and depression (LTD) have been demonstrated with the gradual resistance switching behaviors observed in long-term plasticity programming. A Verilog-A model is proposed based on the multiple-step resistive switching behavior. By implementing the LTP/LTD parameters, an artificial neural network (ANN) is constructed for the demonstration of handwriting classification using Modified National Institute of Standards and Technology (MNIST) dataset.

Artificial Synaptic Behavior of Aloe Polysaccharides-Based Device with Au as Top Electrode

MRS Advances ◽  
2019 ◽  
Vol 5 (14-15) ◽  
pp. 693-698
Author(s):  
Z. X. Lim ◽  
I. A. Tayeb ◽  
Z. A. A. Hamid ◽  
M. F. Ain ◽  
A. M. Hashim ◽  
...  
Keyword(s):  
Metal Structure ◽  
Long Term Potentiation ◽  
Short Term ◽  
Short Term Plasticity ◽  
Voltage Sweep ◽  
Artificial Synapse ◽  
Term Potentiation ◽  
Metal Insulator Metal ◽  
Post Tetanic Potentiation

ABSTRACTFormulated, processed, and dried Aloe polysaccharides thin film sandwiched between ITO as bottom electrode and Au as top electrode has been adopted as an artificial synapse to emulate behavior of neuromorphic computing. The synaptic plasticity or weight has been modulated with this simple metal-insulator-metal structure by applying voltage sweep and voltage pulse, with excitatory postsynaptic current being monitored. Synaptic potentiation and depression has been demonstrated by applying 6 consecutive sweeps of voltage in positive and negative polarity, respectively. By varying number (10 – 50) of voltage pulses, variable synaptic weight has been measured with paired pulse facilitation and post-tetanic potentiation indexes of 2.61x10-6and 1.45x10-4, respectively. The short-term plasticity and long-term potentiation can be clearly revealed when applying 40 pulses and beyond, with extracted time constants of approximately 28 s at 40 pulses and 90 s at 50 pulses.

Ferroelectric polymer-based artificial synapse for neuromorphic computing

2021 ◽  
Author(s):  
Sungjun Kim ◽  
Keun Heo ◽  
Sunghun Lee ◽  
Seunghwan Seo ◽  
Hyeongjun Kim ◽  
...  
Keyword(s):  
Field Effect ◽  
Field Effect Transistor ◽  
Neuromorphic Computing ◽  
Ferroelectric Polymer ◽  
Artificial Synapse ◽  
Effect Transistor

Recently, various efforts have been made to implement synaptic characteristics with a ferroelectric field-effect transistor (FeFET), but in-depth physical analyses have not been reported thus far.

scholarly journals High-Performance Resistive Switching in Solution-Derived IGZO:N Memristors by Microwave-Assisted Nitridation

Nanomaterials ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1081
Author(s):  
Shin-Yi Min ◽  
Won-Ju Cho
Keyword(s):  
Resistive Switching ◽  
Photoelectron Spectroscopy ◽  
High Performance ◽  
Electric Pulse ◽  
Chemical Species ◽  
Optical Absorption Coefficient ◽  
Microwave Assisted ◽  
Synthesis Strategy ◽  
Metal Insulator Metal ◽  
Switching Characteristics

In this study, we implemented a high-performance two-terminal memristor device with a metal/insulator/metal (MIM) structure using a solution-derived In-Ga-Zn-Oxide (IGZO)-based nanocomposite as a resistive switching (RS) layer. In order to secure stable memristive switching characteristics, IGZO:N nanocomposites were synthesized through the microwave-assisted nitridation of solution-derived IGZO thin films, and the resulting improvement in synaptic characteristics was systematically evaluated. The microwave-assisted nitridation of solution-derived IGZO films was clearly demonstrated by chemical etching, optical absorption coefficient analysis, and X-ray photoelectron spectroscopy. Two types of memristor devices were prepared using an IGZO or an IGZO:N nanocomposite film as an RS layer. As a result, the IGZO:N memristors showed excellent endurance and resistance distribution in the 103 repeated cycling tests, while the IGZO memristors showed poor characteristics. Furthermore, in terms of electrical synaptic operation, the IGZO:N memristors possessed a highly stable nonvolatile multi-level resistance controllability and yielded better electric pulse-induced conductance modulation in 5 × 102 stimulation pulses. These findings demonstrate that the microwave annealing process is an effective synthesis strategy for the incorporation of chemical species into the nanocomposite framework, and that the microwave-assisted nitridation improves the memristive switching characteristics in the oxide-based RS layer.

scholarly journals Multistate resistive switching behaviors for neuromorphic computing in memristor

2021 ◽  
Vol 9 ◽  
pp. 100125
Author(s):  
B. Sun ◽  
S. Ranjan ◽  
G. Zhou ◽  
T. Guo ◽  
Y. Xia ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Neuromorphic Computing

scholarly journals Control of the set and reset voltage polarity in anti-series and anti-parallel resistive switching structures

2019 ◽  
Vol 216 ◽  
pp. 111083
Author(s):  
Héctor García ◽  
Luis Antonio Domínguez ◽  
Helena Castán ◽  
Salvador Dueñas
Keyword(s):  
Resistive Switching ◽  
Reset Voltage ◽  
Voltage Polarity

scholarly journals Multiple Evidence for Climate Patterns Influencing Ecosystem Productivity across Spatial Gradients in the Venice Lagoon

2021 ◽  
Vol 9 (4) ◽  
pp. 363
Author(s):  
Camilla Bertolini ◽  
Edouard Royer ◽  
Roberto Pastres
Keyword(s):  
Time Series ◽  
Primary Productivity ◽  
Clustering Analysis ◽  
Venice Lagoon ◽  
Spatial Effects ◽  
Spatial Gradients ◽  
Climate Forecasting ◽  
Oxygen Production Rate ◽  
Temporal And Spatial

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

Enhanced resistance switching in ultrathin Ag/SrTiO3/(La,Sr)MnO3 memristors and their long-term plasticity for neuromorphic computing

2021 ◽  
Vol 119 (2) ◽  
pp. 023502
Author(s):  
Haihua Hu ◽  
Yuke Li ◽  
Yihao Yang ◽  
Wenxin Lv ◽  
Han Yu ◽  
...  
Keyword(s):  
Resistance Switching ◽  
Neuromorphic Computing ◽  
Enhanced Resistance

Reliable analog resistive switching behaviors achieved using memristive devices in AlOx/HfOx bilayer structure for neuromorphic systems

2021 ◽  
Author(s):  
Meng Qi ◽  
Tianquan Fu ◽  
Huadong Yang ◽  
ye tao ◽  
Chunran Li ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Learning Experience ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Spike Timing ◽  
Information Storage ◽  
Bilayer Structure ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Simulation Based

Abstract Human brain synaptic memory simulation based on resistive random access memory (RRAM) has an enormous potential to replace traditional Von Neumann digital computer thanks to several advantages, including its simple structure, high-density integration, and the capability to information storage and neuromorphic computing. Herein, the reliable resistive switching (RS) behaviors of RRAM are demonstrated by engineering AlOx/HfOx bilayer structure. This allows for uniform multibit information storage. Further, the analog switching behaviors are capable of imitate several synaptic learning functions, including learning experience behaviors, short-term plasticity-long-term plasticity transition, and spike-timing-dependent-plasticity (STDP). In addition, the memristor based on STDP learning rules are implemented in image pattern recognition. These results may offer a promising potential of HfOx-based memristors for future information storage and neuromorphic computing applications.

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