Bipolar resistance switching property of Al-Ag/La0.7Ca0.3MnO3/Pt sandwiches

Liu, Xin Jun; Li, X. M.; Yu, Weidong; Wang, Q.; Yang, Rui; Cao, Xun; Chen, Lidong

doi:10.2109/jcersj2.117.732

Cited by 5 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lanthanum Strontium Manganite : Oxides with perovskite structures have been reported as dielectrics of threshold switches and CBRAMs . Liu et al observed unidirectional threshold switching in a Pt/ a ‐La 0.3 Mn 0.7 SrO 3 /Ag system, where both electrodes and dielectric film were fabricated by sputtering (see Figure b).…”

Section: Methodsmentioning

confidence: 99%

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

Wang

Rao

Midya

et al. 2017

Adv Funct Materials

277

233

View full text Add to dashboard Cite

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...

show abstract

Section: Methodsmentioning

confidence: 99%

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

Wang

Rao

Midya

et al. 2017

Adv Funct Materials

277

233

View full text Add to dashboard Cite

show abstract

“…The general structure of RRAM is shown in figure 8(a). Common electrode materials of RRAMs can be divided into five categories based on their composition or contribution to the resistive switching (RS) behavior, including elementary substance electrodes [59][60][61], alloy electrodes [62], silicon-based electrodes [63], nitridebased electrodes [64], and oxide-based electrodes [65]. On the other hand, storage media can be classified into organic storage media [66,67] and inorganic storage media [68].…”

Section: Embedded Rrammentioning

confidence: 99%

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Wang,

Song,

Zhang

et al. 2024

Int. J. Extrem. Manuf.

View full text Add to dashboard Cite

Embedded memory, which heavily relies on the manufacturing process, has been widely adopted in various industrial applications. As the field of embedded memory continues to evolve, innovative strategies are emerging to enhance performance. Among them, resistive random access memory (RRAM) has gained significant attention due to its numerous advantages over traditional memory devices, including high speed (<1ns), high density (4F2/n), high scalability (~nm), and low power consumption (~pJ). This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potential applications. It provides a brief introduction to the concepts and advantages of RRAM, discusses the key factors that impact its industrial manufacturing, and presents the commercial progress driven by cutting-edge nanotechnology, which has been pursued by many semiconductor giants. Additionally, it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things (IoT) and future intelligent computing, with a particular emphasis on its role in neuromorphic computing. Finally, the review discusses the current challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence (AI).

show abstract