An electronic silicon-based memristor with a high switching uniformity

Lu, Yang; Álvarez, Ana María; Kao, Chung-Ho; Bow, J.S.; Chen, San‐Yuan; Chen, I‐Wei

doi:10.1038/s41928-019-0204-7

Cited by 61 publications

(48 citation statements)

References 38 publications

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“…Amorphous silicon presents such interesting resistance switching behavior . Recently, amorphous‐silicon‐based memristors with excellent performance were demonstrated, which have promising advantages for developing artificial intelligence chips (AI chips) . During the near‐data computing process in next‐generation AI chips, the very large amount of Joule heat generated results in a challenge for heat dissipation.…”

Section: Introductionmentioning

confidence: 99%

Thermal Conductivity of Amorphous Materials

Zhou

Cheng

Chen

et al. 2019

Adv Funct Materials

182

110

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Thermal conductivity is one of the most fundamental properties of solid materials. The thermal conductivity of ideal crystal materials has been widely studied over the past hundreds years. On the contrary, for amorphous materials that have valuable applications in flexible electronics, wearable electrics, artificial intelligence chips, thermal protection, advanced detectors, thermoelectrics, and other fields, their thermal properties are relatively rarely reported. Moreover, recent research indicates that the thermal conductivity of amorphous materials is quite different from that of ideal crystal materials. In this article, the authors systematically review the fundamental physical aspects of thermal conductivity in amorphous materials. They discuss the method to distinguish the different heat carriers (propagons, diffusons, and locons) and the relative contribution from them to thermal conductivity. In addition, various influencing factors, such as size, temperature, and interfaces, are addressed, and a series of interesting anomalies are presented. Finally, the authors discuss a number of open problems on thermal conductivity of amorphous materials and a brief summary is provided.

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Section: Introductionmentioning

confidence: 99%

Thermal Conductivity of Amorphous Materials

Zhou

Cheng

Chen

et al. 2019

Adv Funct Materials

182

110

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“…These two materials are applied in some devices [1][2][3]. Nowadays, they are used as nanometallic memristors [4], photonic devices [5,6], optoelectronic devices [7], and spin-qubit devices [8][9][10]. More devices are also being developed, indicating the need for better knowledge of the material properties of the elements of these devices.…”

Section: Introductionmentioning

confidence: 99%

Optical properties of silicon and germanium determined by high-precision analysis of reflection electron energy loss spectroscopy spectra

et al. 2019

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We present a detailed analysis and comparison of four models describing the extension of the electron-energy loss function from the optical limit of q→0 into the (q,ω) plane to obtain the bulk and surface terms of differential inverse inelastic mean free paths. We found that the best model that describes accurately and times efficiently the calculation of the energy loss function of free-electron-like materials is the combination of the Penn algorithm [Phys. Rev. B 35, 482 (1987)] with the Ritchie-Howie method [Philos. Mag. 36, 463 (1977)]. Applying this model in our reverse Monte Carlo method, we determined, with high-precision, electron-energy loss functions of silicon and germanium based on the theoretical analysis of the high-energy resolution reflected electron energy loss spectroscopy (REELS) spectra, measured at 3, 4, and 5 keV incident electron energies. The refractive index n, the extinction coefficient k, and the complex dielectric function (ε = ε 1 + iε 2 ) were calculated from the obtained energy loss function in a wide energy loss range of 0-200 eV. The accuracy of the obtained results is justified with various sum rules. We found that the calculated optical data of Si and Ge fulfill the sum rules with an average accuracy of 0.11% or even better. Therefore, the use of these optical data in materials science and surface analysis is highly recommended for further applications.

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“…[ 21 ] The search for materials and devices that can enable spatially homogeneous, forming‐free, reproducible, and consistent memristors have long been pursued, which could solve problems inherent to stochastic mechanisms. [ 6,9,22 ]…”

Section: Figurementioning

confidence: 99%

“…[21] The search for materials and devices that can enable spatially homogeneous, forming-free, reproducible, and consistent memristors have long been pursued, which could solve problems inherent to stochastic mechanisms. [6,9,22] Among oxide memristors, there have been several attempts to realize uniform switching. [20,22] There are reports demonstrating areal current scaling by using different electrode dimensions, [20,[22][23][24] but those observations are indirect and have not yielded a general material or device engineering strategy to reliably produce homogeneous switching.…”

mentioning

confidence: 99%

Nanometer‐Scale Uniform Conductance Switching in Molecular Memristors

et al. 2020

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energy-efficient, high-density data storage, and computing are the subject of intense research. [1-3] Memristors are promising building blocks for the next generation of electronics because they enable emerging highly efficient computing platforms such as in-memory and neuromorphic computing. [4,5] Of all genres of memristors, nonvolatile oxide materials are believed to be the most successful candidates in terms of their commercial potential. All the oxide memristors in use today are primarily based on filamentary mechanisms, which are inherently nonuniform [6-8] and thus exhibit performance variability and compromised device-yield. [9] In fact, the issue of nonuniformity and stochasticity is not only specific to oxides but is common in other genres of memristors like phase change memories, [10] nitrides, [11] or those working on metal-ion migration. [12-14] One of the problematic features inherent to any filamentary mechanism is electroforming, which is the first-time switching process involving dissolution, injection, and orientation of the active conducting atoms/ions into the dielectric layer. [7,15-17] The forming process usually requires much higher voltage and current than the reading/writing processes, resulting in One common challenge highlighted in almost every review article on organic resistive memory is the lack of areal switching uniformity. This, in fact, is a puzzle because a molecular switching mechanism should ideally be isotropic and produce homogeneous current switching free from electroforming. Such a demonstration, however, remains elusive to date. The reports attempting to characterize a nanoscopic picture of switching in molecular films show random current spikes, just opposite to the expectation. Here, this longstanding conundrum is resolved by demonstrating 100% spatially homogeneous current switching (driven by molecular redox) in memristors based on Ru-complexes of azo-aromatic ligands. Through a concurrent nanoscopic spatial mapping using conductive atomic force microscopy and in operando tip-enhanced Raman spectroscopy (both with resolution <7 nm), it is shown that molecular switching in the films is uniform from hundreds of micrometers down to the nanoscale and that conductance value exactly correlates with spectroscopically determined molecular redox states. This provides a deterministic molecular route to obtain spatially homogeneous, forming-free switching that can conceivably overcome the chronic problems of robustness, consistency, reproducibility, and scalability in organic memristors.

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An electronic silicon-based memristor with a high switching uniformity

Cited by 61 publications

References 38 publications

Thermal Conductivity of Amorphous Materials

Thermal Conductivity of Amorphous Materials

Optical properties of silicon and germanium determined by high-precision analysis of reflection electron energy loss spectroscopy spectra

Nanometer‐Scale Uniform Conductance Switching in Molecular Memristors

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