Control of Cu Conductive Filament in Complementary Atom Switch for Cross-Point Selector Device Application

Woo, Jiyong; Lee, Daeseok; Cha, Euijun; Lee, Sangheon; Park, Sangsu; Hwang, Hyunsang

doi:10.1109/led.2013.2290120

Cited by 33 publications

(25 citation statements)

References 12 publications

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“…Titania : Being paired with either Ag or Cu electrodes, TiO 2 has exhibited threshold switching in both planar structure and vertical stacks . Song et al developed a Pt/TiO 2 /Ag cell with a dielectric film grown by atomic layer deposition (ALD) and a sputtered Ag electrode, featuring bidirectional threshold switching with <10 ns transition time (see Figure d).…”

Section: Methodsmentioning

confidence: 99%

“…Copyright 2015, Elsevier Ltd.) Relaxation dependence on the programming voltage/current of a m) Pt/TiO x /Cu, n) Pt/a‐La 0.3 Mn 0.7 SrO 3 /Ag, o) Pt/SiO x N y :Ag/Pt threshold switch. ((m) Reproduced with permission . Copyright 2014, IEEE.…”

Section: Temporal Response Of the Switchingmentioning

confidence: 99%

“…High programming voltage/current could potentially strengthen the filament which becomes difficult to be ruptured, resulting in a long relaxation or even nonvolatile switching. On the other hand, a limited programming voltage/current may lead to a confined volume of the filament, which is easier to be annihilated by the restoring force …”

Section: Temporal Response Of the Switchingmentioning

confidence: 99%

“…As shown in Figure m, Woo et al reported a quasi‐linear relationship between the compliance current used in programming and relaxation or retention time in a Pt/TiO x /Cu cell. The relaxation took about ≈20 s at 1 µA and ≈800 s at 1 mA …”

Section: Temporal Response Of the Switchingmentioning

confidence: 99%

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Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Wang

Rao

Midya

et al. 2017

Adv Funct Materials

293

233

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Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.is applied due to the formation of a conduction channel(s) with Ag or Cu atoms. Unlike the ECM cells, the resistance recovers back spontaneously upon cessation of the external bias, yielding a superior I-V nonlinearity [4][5][6][7][8][9][10][11] and unique temporal conductance evolution dynamics. [7,12,13] Such a relaxation process is due to the physical dissolution of the metallic conduction channel under driving forces such as minimization of interfacial energy. In case active metals are used as electrodes, these metals may be doped into the dielectrics eventually under the combined effect of electric fields, thermal diffusion, which may lead to a reduced threshold voltage for the subsequent switching, similar to the process called "electroforming." The unique delay and relaxation dynamics of Ag and Cu-based threshold switches make them suitable for innovative applications in circuits and systems.Threshold switches with Ag or Cu active metals are also termed as "diffusive memristors" [7] to emphasize the underlying nature of the diffusive dynamics of the metal species. Factors including bias amplitude, biasing duration, as well as ambient temperature have been observed to have an impact on such a process, showing a wide range of dynamical properties, which could be exploited as access devices for memories with fast transition (e.g., <100 ns) or synaptic emulators with a relatively slower evolution (e.g., >1 µs). We survey the recently developed material systems which have exhibited this kind of threshold switching. New evidences by electron microscopy and quantitatively thermodynamic modeling are examined to give insights into the underlying physics of the mechanisms. We also discuss applications enabled by the advent of such threshold switches. Temporal Response of the SwitchingThe dynamical response of threshold switching is a critical property for many applications but has been characterized to a lesser extent. The temporal responses could be probed by applying voltage pulses and measuring the resulting currents in the time domain. It is a general observation in both ECM and threshold switches that the conductance experienced a transition from insulating state to conducting state after a finite time duration (delay time) under the external bias, as ill...

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Section: Methodsmentioning

confidence: 99%

Section: Temporal Response Of the Switchingmentioning

confidence: 99%

Section: Temporal Response Of the Switchingmentioning

confidence: 99%

Section: Temporal Response Of the Switchingmentioning

confidence: 99%

See 2 more Smart Citations

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Wang

Rao

Midya

et al. 2017

Adv Funct Materials

293

233

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show abstract

“…The cation migration–based memristive devices are generally referred to as atomic switch or programmable metallization cell . or electrochemical metallization cell (ECM) Besides metal ions, anions migration, most commonly oxygen ions are involved in the memristive devices based on transition metal oxides without electrochemical active electrodes . The migration of oxygen ions usually induces a redox reaction expressed by a valence change of the cation sublattice and leads to a stoichiometry change of the oxides.…”

Section: Memristive Devicesmentioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

186

127

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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A Memristor‐Based Silicon Carbide for Artificial Nociceptor and Neuromorphic Computing

Liu

Zhao

Cao

et al. 2021

Adv Materials Technologies

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With the advancement of artificial intelligence technology, more and more biological functions need to be imitated to complete more complex tasks and adapt to a complex external work environment. Memristors, as an excellent candidate for neuromorphic artificial electronic devices with many biological functions, have inspired the interest of researchers because of the advantages of scalability, good retention, and high operating speed. In this work, wide band gap semiconductor materials silicon carbide (SiC) films are prepared as a memristor medium. By adjusting the current compliance, both threshold character and bipolar resistive switching phenomenon are realized in one device with both lower powers for set operation. For the threshold characteristic, this device has mimicked the “threshold,” “inadaptation,” and “relaxation” features of a nociceptor, which will protect the artificial intelligence system to have stronger adaptability to the external environment. For the bipolar resistive switching characteristics, this device demonstrates good stability and retention time, with a switching speed of 18 ns. These bipolar resistance switching characteristics have simulated many synaptic functions. Pulses with hundreds of nanosecond time scale widths are conducive to fast learning and calculation. This device‐based third‐generation SiC semiconductor material will find a broad application in neuromorphic chip systems.

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Control of Cu Conductive Filament in Complementary Atom Switch for Cross-Point Selector Device Application

Cited by 33 publications

References 12 publications

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Memristive Synapses and Neurons for Bioinspired Computing

A Memristor‐Based Silicon Carbide for Artificial Nociceptor and Neuromorphic Computing

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