bipolar switching
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Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 98
Author(s):  
Eugeny Ryndin ◽  
Natalia Andreeva ◽  
Victor Luchinin

The article presents the results of the development and study of a combined circuitry (compact) model of thin metal oxide films based memristive elements, which makes it possible to simulate both bipolar switching processes and multilevel tuning of the memristor conductivity taking into account the statistical variability of parameters for both device-to-device and cycle-to-cycle switching. The equivalent circuit of the memristive element and the equation system of the proposed model are considered. The software implementation of the model in the MATLAB has been made. The results of modeling static current-voltage characteristics and transient processes during bipolar switching and multilevel turning of the conductivity of memristive elements are obtained. A good agreement between the simulation results and the measured current-voltage characteristics of memristors based on TiOx films (30 nm) and bilayer TiO2/Al2O3 structures (60 nm/5 nm) is demonstrated.


Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2684
Author(s):  
Yeon Pyo ◽  
Jong-Un Woo ◽  
Hyun-Gyu Hwang ◽  
Sahn Nahm ◽  
Jichai Jeong

An amorphous Pr0.7Ca0.3MnO3 (PCMO) film was grown on a TiN/SiO2/Si (TiN–Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.


Author(s):  
Yu Yan ◽  
Jia Cheng Li ◽  
Yu Ting Chen ◽  
Xiang Yu Wang ◽  
Gang Ri Cai ◽  
...  
Keyword(s):  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christiane Ader ◽  
Andreas Falkenstein ◽  
Manfred Martin

AbstractResistive switching is an important phenomenon for future memory devices such as resistance random access memories or neuronal networks. While there are different types of resistive switching, such as filament or interface switching, this work focuses on bulk switching in amorphous, binary oxides. Bulk switching was found experimentally in different oxides, for example in amorphous gallium oxide. The forms of the observed current–voltage curves differ, however, fundamentally. Even within the same material, both abnormal bipolar and normal bipolar resistive switching were found. Here, we use a new drift–diffusion model to theoretically investigate bulk switching in amorphous oxides where the electronic conductivity can be described by Mott’s concept of a mobility edge. We show not only that a strong, non-linear dependence of the electronic conductivity on the oxygen content is necessary for bulk switching but also that changing the geometry of the memristive device causes the transition between abnormal and normal bipolar switching.


2021 ◽  
Vol 15 ◽  
Author(s):  
Christopher Bengel ◽  
Felix Cüppers ◽  
Melika Payvand ◽  
Regina Dittmann ◽  
Rainer Waser ◽  
...  

With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data, neuromorphic chip concepts are seen as key solutions for coping with the massive amount of unstructured data streams by moving the computation closer to the sensors, the so-called “edge computing.” Augmenting these chips with emerging memory technologies enables these edge devices with non-volatile and adaptive properties which are desirable for low power and online learning operations. However, an energy- and area-efficient realization of these systems requires disruptive hardware changes. Memristor-based solutions for these concepts are in the focus of research and industry due to their low-power and high-density online learning potential. Specifically, the filamentary-type valence change mechanism (VCM memories) have shown to be a promising candidate In consequence, physical models capturing a broad spectrum of experimentally observed features such as the pronounced cycle-to-cycle (c2c) and device-to-device (d2d) variability are required for accurate evaluation of the proposed concepts. In this study, we present an in-depth experimental analysis of d2d and c2c variability of filamentary-type bipolar switching HfO2/TiOx nano-sized crossbar devices and match the experimentally observed variabilities to our physically motivated JART VCM compact model. Based on this approach, we evaluate the concept of parallel operation of devices as a synapse both experimentally and theoretically. These parallel synapses form a synaptic array which is at the core of neuromorphic chips. We exploit the c2c variability of these devices for stochastic online learning which has shown to increase the effective bit precision of the devices. Finally, we demonstrate that stochastic switching features for a pattern classification task that can be employed in an online learning neural network.


2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hau Huu Do Ho ◽  
Trung Minh Le ◽  
Ngoc Kim Pham

Resistive random access memory (RRAM) is emerging as a new class of nonvolatile memory that offers promising electronic properties and simple metal-insulator-metal (MIM) structures for sandwich layers, such as organics, inorganics, and hybrid materials. Hybrid structures have attracted much interest recently because of their advantageous properties. The combination of chitosan (CS) and graphene oxide (GO) acts as switching layers in the Al/CS-GO/FTO RRAM structure it is studied with bipolar switching behavior at approximately 102 ON/OFF ratios during 100 cycles. This hybrid interaction is identified by shifts in the D, G, and 2D bands using Raman spectroscopy. The conduction mechanism is proposed to be a space-charge-limited conduction (SCLC) mechanism and trap-assisted tunneling conduction mechanism in the ON and OFF states, respectively. The trapped and detrapped electrons move through the trap sites with external electric fields, and this movement is responsible for the switching mechanism of the CS-GO nanocomposite memory device.


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Sanchali Mitra ◽  
Arnab Kabiraj ◽  
Santanu Mahapatra

AbstractResistive-memory devices promise to revolutionize modern computer architecture eliminating the data-shuttling bottleneck between the memory and processing unit. Recent years have seen a surge of experimental demonstrations of such devices built upon two-dimensional materials based metal–insulator–metal structures. However, the fundamental mechanism of nonvolatile resistive switching has remained elusive. Here, we conduct reactive molecular dynamics simulations for a sulfur vacancy inhabited monolayer molybdenum disulfide-based device with inert electrode systems to gain insight into such phenomena. We observe that with the application of a suitable electric field, at the vacancy positions, the sulfur atom from the other plane pops and gets arrested in the plane of the molybdenum atoms. Rigorous first principles based calculations surprisingly reveal localized metallic states (virtual filament) and stronger chemical bonding for this new atomic arrangement, explaining the nonvolatile resistive switching. We further observe that localized Joule heating plays a crucial role in restoring the popped sulfur atom to its original position. The proposed theory, which delineates both unipolar and bipolar switching, may provide useful guidelines for designing high-performance resistive-memory-based computing architecture.


Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 306
Author(s):  
Panagiotis Bousoulas ◽  
Charalampos Papakonstantinopoulos ◽  
Stavros Kitsios ◽  
Konstantinos Moustakas ◽  
Georgios Ch. Sirakoulis ◽  
...  

The quick growth of information technology has necessitated the need for developing novel electronic devices capable of performing novel neuromorphic computations with low power consumption and a high degree of accuracy. In order to achieve this goal, it is of vital importance to devise artificial neural networks with inherent capabilities of emulating various synaptic properties that play a key role in the learning procedures. Along these lines, we report here the direct impact of a dense layer of Pt nanoparticles that plays the role of the bottom electrode, on the manifestation of the bipolar switching effect within SiO2-based conductive bridge memories. Valuable insights regarding the influence of the thermal conductivity value of the bottom electrode on the conducting filament growth mechanism are provided through the application of a numerical model. The implementation of an intermediate switching transition slope during the SET transition permits the emulation of various artificial synaptic functionalities, such as short-term plasticity, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights toward the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior.


2021 ◽  
Vol 54 (22) ◽  
pp. 225303 ◽  
Author(s):  
Panagiotis Bousoulas ◽  
Marianthi Panagopoulou ◽  
Nikos Boukos ◽  
Dimitris Tsoukalas

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