scholarly journals Effect of Surface Variations on Resistive Switching

2021 ◽  
Author(s):  
Mangal Das ◽  
Sandeep Kumar
Keyword(s):  
Grain Boundaries ◽  
Surface Properties ◽  
Resistive Switching ◽  
Current Flow ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Charge Carriers ◽  
Barrier Surface ◽  
Flow Through ◽  
Effect Of Surface

In this chapter, we study factors that dominate the interfacial resistive switching (RS) in memristive devices. We have also given the basic understanding of different type of RS devices which are predominantly interfacial in nature. In case of resistive random access memory (RRAM), the effect of surface properties on the bulk cannot be neglected as thickness of the film is generally below 100 nm. Surface properties are effected by redox reactions, interfacial layer formation, and presence of tunneling barrier. Surface morphology affects the band structure in the vicinity of interface, which in turn effects the movements of charge carriers. The effect of grain boundaries (GBs) and grain surfaces (GSs) on RS have also been discussed. The concentration of vacancies (Ov)/traps/defects is comparatively higher at GBs which leads to leakage current flow through the GBs predominantly. Such huge presence of charge carriers causes current flow through grain boundaries.

Experimental and Theoretical Studies of Resistive Switching in Grain Boundaries of Polycrystalline Transition Metal Oxide Film

MRS Advances ◽  
2017 ◽  
Vol 2 (4) ◽  
pp. 229-234
Author(s):  
Takumi Moriyama ◽  
Sohta Hida ◽  
Takahiro Yamasaki ◽  
Takahisa Ohno ◽  
Satoru Kishida ◽  
...  
Keyword(s):  
Metal Oxide ◽  
Grain Boundaries ◽  
Resistive Switching ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
First Principles Calculation ◽  
Operating Modes ◽  
Calculation Results ◽  
Grain Surface ◽  
Experimental And Theoretical Studies

ABSTRACTPractical use of Resistive Random Access Memory (ReRAM) depends on thorough understanding of the resistive switching (RS) mechanism in polycrystalline metal oxide films. Based on experimental and theoretical results of NiO based ReRAM, we have proposed a grain surface tiling model, in which grain surfaces (i.e. grain boundaries) are composed by insulating and conductive micro surface structures. This paper reports the adequacy of our model to the NiO based ReRAM and universality of surface electronic properties in metal oxides of NiO, CoO and MgO. Experimental results of RS operating modes suggest that the resistance changes in the grain boundaries, supporting our model. First-principles calculation results suggest that our model can be adopted to other metal oxide materials and the RS from a low resistance to a high resistance can be caused at 1000 K, which agrees with previous experimental reports.

scholarly journals Improved Synaptic Device Properties of HfAlOx Dielectric on Highly Doped Silicon Substrate by Partial Reset Process

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 772
Author(s):  
Seunghyun Kim ◽  
Osung Kwon ◽  
Hojeong Ryu ◽  
Sungjun Kim
Keyword(s):  
Resistive Switching ◽  
Silicon Substrate ◽  
Photoelectron Spectroscopy ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Alloy Type ◽  
Switching Type ◽  
Neuromorphic System ◽  
Device Properties ◽  
Partial Reset

This work demonstrates the synaptic properties of the alloy-type resistive random-access memory (RRAM). We fabricated the HfAlOx-based RRAM for a synaptic device in a neuromorphic system. The deposition of the HfAlOx film on the silicon substrate was verified by X-ray photoelectron spectroscopy (XPS) analysis. It was found that both abrupt and gradual resistive switching could be implemented, depending on the reset stop voltage. In the reset process, the current gradually decreased at weak voltage, and at strong voltage, it tended to decrease rapidly by Joule heating. The type of switching determined by the first reset process was subsequently demonstrated to be stable switching by successive set and reset processes. A gradual switching type has a much smaller on/off window than abrupt switching. In addition, retention maintained stability up to 2000 s in both switching cases. Next, the multiple current states were tested in the gradual switching case by identical pulses. Finally, we demonstrated the potentiation and depression of the Cu/HfAlOx/Si device as a synapse in an artificial neural network and confirmed that gradual resistive switching was suitable for artificial synapses, using neuromorphic system simulation.

Transparent resistive random access memory and its characteristics for nonvolatile resistive switching

2008 ◽  
Vol 93 (22) ◽  
pp. 223505 ◽  
Author(s):  
Jung Won Seo ◽  
Jae-Woo Park ◽  
Keong Su Lim ◽  
Ji-Hwan Yang ◽  
Sang Jung Kang
Keyword(s):  
Resistive Switching ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Access Memory

Resistive Switching Characteristics in TiO2 ReRAM with Top Electrode of Co Selectively Formed on SAMs Printed Patterns

2007 ◽  
Vol 124-126 ◽  
pp. 603-606
Author(s):  
Sang Hee Won ◽  
Seung Hee Go ◽  
Jae Gab Lee
Keyword(s):  
Resistive Switching ◽  
Random Access ◽  
Atomic Layer ◽  
High Resistance State ◽  
Resistive Random Access Memory ◽  
Tio2 Thin Films ◽  
Selective Deposition ◽  
Precise Control ◽  
Resistance State ◽  
Switching Characteristics

Simple process for the fabrication of Co/TiO2/Pt resistive random access memory, called ReRAM, has been developed by selective deposition of Co on micro-contact printed (μ-CP) self assembled monolayers (SAMs) patterns. Atomic Layer Deposition (ALD) was used to deposit TiO2 thin films, showing its ability of precise control over the thickness of TiO2, which is crucial to obtain proper resistive switching properties of TiO2 ReRAM. The fabrication process for Co/TiO2/Pt ReRAM involves the ALD of TiO2 on sputter-deposited Pt bottom electrode, followed by μ-CP with SAMs and then selective deposition of Co. This results in the Co/TiO2/Pt structure ReRAM. For comparison, Pt/TiO2/Pt ReRAM was produced and revealing the similar switching characteristics as that of Co/TiO2/Pt, thus indicating the feasibility of Co replacement with Pt top electrode. The ratios between the high-resistance state (Off state) and the low-resistance state (On state) were larger than 102. Consequently, the selective deposition of Co with μ-CP, newly developed in this study, can simplify the process and thus implemented into the fabrication of ReRAM.

Fabrication of Semi-transparent Resistive Random Access memory and Its Characteristics of Nonvolatile Resistive Switching

2011 ◽  
Vol 1292 ◽  
Author(s):  
Jung Won Seo ◽  
Seung Jae Baik ◽  
Sang Jung Kang ◽  
Koeng Su Lim
Keyword(s):  
Metal Oxide ◽  
Resistive Switching ◽  
Current Density ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Access Memory ◽  
Promising Candidate ◽  
Forward Current ◽  
Switching Characteristics

ABSTRACTThis report covers the resistive switching characteristics of cross-bar type semi-transparent (or see-through) resistive random access memory (RRAM) devices based on ZnO. In order to evaluate the transmittance of the devices, we designed the memory array with various electrode sizes and spaces between the electrodes. To prevent read disturbance problems due to sneak currents, we employed a metal oxide based p-NiO/n-ZnO diode structure, which exhibited good rectifying characteristics and high forward current density. Based on these results, we found that the combined metal oxide diode/RRAM device could be promising candidate with suppressed read disturbances of cross-bar type ZnO RRAM device.

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.

Effect of Threshold Voltage Window and Variation of Organic Synaptic Transistor for Neuromorphic System

2021 ◽  
Vol 21 (8) ◽  
pp. 4303-4309
Author(s):  
Yeongjin Hwang ◽  
Jeong Hoon Jeon ◽  
Juhyun Lee ◽  
Jonghyuk Yoon ◽  
Felix Sunjoo Kim ◽  
...  
Keyword(s):  
Power Consumption ◽  
Threshold Voltage ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Classification Rate ◽  
Data Set ◽  
Organic Transistor ◽  
Handwritten Digit ◽  
Flow Through ◽  
Neuromorphic System

Synaptic devices, which are considered as one of the most important components of neuromorphic system, require a memory effect to store weight values, a high integrity for compact system, and a wide window to guarantee an accurate programming between each weight level. In this regard, memristive devices such as resistive random access memory (RRAM) and phase change memory (PCM) have been intensely studied; however, these devices have quite high current-level despite their state, which would be an issue if a deep and massive neural network is implemented with these devices since a large amount of current-sum needs to flow through a single electrode line. Organic transistor is one of the potential candidates as synaptic device owing to flexibility and a low current drivability for low power consumption during inference. In this paper, we investigate the performance and power consumption of neuromorphic system composed of organic synaptic transistors conducting a pattern recognition simulation with MNIST handwritten digit data set. It is analyzed according to threshold voltage (VT) window, device variation, and the number of available states. The classification accuracy is not affected by VT window if the device variation is not considered, but the current sum ratio between answer node and the rest 9 nodes varies. In contrast, the accuracy is significantly degraded as increasing the device variation; however, the classification rate is less affected when the number of device states is fewer.

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