Electrical Properties and Fatigue Behaviors of ZrO[sub 2] Resistive Switching Thin Films

Lin, Chih-Yang; Wang, Sheng-Yi; Lee, Dai-Ying; Tseng, Tseung‐Yuen

doi:10.1149/1.2946430

Cited by 82 publications

(54 citation statements)

References 30 publications

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“…In contrast, the HRS conductive mechanism is dominated by SCLC. Similar combined mechanisms in an RRAM device are reported in other work [33].…”

Section: Discussionsupporting

confidence: 87%

“…The typical I-V characteristic of trap-controlled SCLC generally consists of two different regions, as shown in Figure 5, as reported by Lin et al [33]. In the low voltage region, the I-V characteristics exhibit ohmic conduction (I∝V).…”

Section: Space-charge-limited Conductionsupporting

confidence: 58%

“…Work has confirmed that resistive switching generally involves, rather than one mechanism, a combination of several different mechanisms [33]. Figure 6 shows an example of a combination of mechanisms in our fabricated Cu/WO 3 /Pt RRAM device [5].…”

Section: Discussionmentioning

confidence: 86%

“…It has been reported that certain I-V relationships can be used to identify the underlying resistive-switching mechanisms [27][28][29][30][31][32][33], which can be mainly divided into: formation and rupture of conductive filaments, space-charge-limited conduction, trap charging and discharging, Schottky Emission, and Pool-Frenkel emission.…”

Section: Resistive-switching Mechanismsmentioning

confidence: 99%

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An overview of resistive random access memory devices

Long

Liu

et al. 2011

Chin. Sci. Bull.

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With recent progress in material science, resistive random access memory (RRAM) devices have attracted interest for nonvolatile, low-power, nondestructive readout, and high-density memories. Relevant performance parameters of RRAM devices include operating voltage, operation speed, resistance ratio, endurance, retention time, device yield, and multilevel storage. Numerous resistive-switching mechanisms, such as conductive filament, space-charge-limited conduction, trap charging and discharging, Schottky Emission, and Pool-Frenkel emission, have been proposed to explain the resistive switching of RRAM devices. In addition to a discussion of these mechanisms, the effects of electrode materials, doped oxide materials, and different configuration devices on the resistive-switching characteristics in nonvolatile memory applications, are reviewed. Finally, suggestions for future research, as well as the challenges awaiting RRAM devices, are given.

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“…In contrast, the HRS conductive mechanism is dominated by SCLC. Similar combined mechanisms in an RRAM device are reported in other work [33].…”

Section: Discussionsupporting

confidence: 87%

Section: Space-charge-limited Conductionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 86%

Section: Resistive-switching Mechanismsmentioning

confidence: 99%

See 2 more Smart Citations

An overview of resistive random access memory devices

Long

Liu

et al. 2011

Chin. Sci. Bull.

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show abstract

“…SCLC conduction mechanism of HRS is often reported in ReRAM materials with bipolar resistive switching behaviors, such as in Au/Cr/Zr + -implanted-ZrO 2 /n + -Si capacitors [31] and in Ti/ZrO 2 /Pt capacitors [32]. SCLC conduction observed in unipolar SZO capacitors implies that oxygen vacancies formed in perovskite SZO matrix are more mobile.…”

Section: Resultsmentioning

confidence: 97%

Current–voltage characteristics of sol–gel derived SrZrO3 thin films for resistive memory applications

Wen

et al. 2011

Journal of Alloys and Compounds

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The Future of Memristors: Materials Engineering and Neural Networks

Sun

Chen

Yan

2020

Adv Funct Materials

274

173

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From Deep Blue to AlphaGo, artificial intelligence and machine learning are booming, and neural networks have become the hot research direction. However, due to the size limit of complementary metal–oxide–semiconductor (CMOS) transistors, von Neumann‐based computing systems are facing multiple challenges (such as memory walls). As the number of transistors required by the neural network increases, the development of neural networks based on the von Neumann computer is limited by volume and energy consumption. As the fourth basic circuit element, memristor shines in the field of neuromorphic computing. The new computer architecture based on memristor is widely considered as a substitute for the von Neumann architecture and has great potential to deal with the neural network and big data era challenge. This article reviews existing materials and structures of memristors, neurophysiological simulations based on memristors, and applications of memristor‐based neural networks. The feasibility and advancement of implementing neural networks using memristors are discussed, the difficulties that need to be overcome at this stage are put forward, and their development prospects and challenges faced are also discussed.

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Electrical Properties and Fatigue Behaviors of ZrO[sub 2] Resistive Switching Thin Films

Cited by 82 publications

References 30 publications

An overview of resistive random access memory devices

An overview of resistive random access memory devices

Current–voltage characteristics of sol–gel derived SrZrO3 thin films for resistive memory applications

The Future of Memristors: Materials Engineering and Neural Networks

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