Control of topography, stress and diffusion at molecule–metal interfaces

Zhitenev, Nikolai B.; Jiang, W.; Erbe, Artur; Bao, Zhenan; Garfunkel, Eric; Tennant, D. M.; Cirelli, Raymond A.

doi:10.1088/0957-4484/17/5/019

Cited by 28 publications

(34 citation statements)

References 27 publications

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“…One reason is that the Simmons model does not account for the reduced density of states in the Si electrode compared to a metallic one. Nevertheless, most molecular MIM show much lower conductance values [96,157,158] than the theoretically predicted one (G C (¼ lim…”

Section: Extracting Insulator Parameters From Current-density-voltagementioning

confidence: 97%

“…also Section 7.2, below). [34,67,69,70,92,[94][95][96] Beyond structure and morphology, several surface analyses, mainly spectroscopic ones, can provide information on the electronic structure of the system. As such information cannot be extracted directly from the transport data, the use of complementary methods is crucial for understanding transport mechanisms.…”

Section: Monolayer Characterizationmentioning

confidence: 99%

“…[96,132,133] To control both transport and dipole effects, it seems therefore best to start from a transport quality monolayer that passivates the surface chemically and electronically, and then add polar (or other functional) moieties that do not affect the integrity of the interface. [49,[134][135][136] …”

Section: Molecular Dipolementioning

confidence: 99%

“…Reported temperature dependence of transport varies from none [55,172,174,175] to positive (i.e., thermal activation). [34,55,96] In MIM junctions, a pseudo-metallic temperature response is generally attributed to conductance via metallic filaments penetrating the monolayer. [34,171] This is very unlikely here, because if we change the insulator by electron irradiation [164] (or ''dope'' it, [139] ) the metal-like dependence is replaced by a thermally activated behavior, as shown in Figure 7b (dotted lines).…”

Section: Quasi-metallic Temperature Behavior For Monolayer-limited Cumentioning

confidence: 99%

“…It also creates inhomogeneities in the work function of the substrate [183,184] and, thus, creates an inhomogeneous electric field between the contacts. Reducing the roughness of Au [96] or Ag [133] drastically improved the yield of non-shorted MIM's and the reproducibility of the measured currents, respectively. Bacteriorhodopsin monolayers on a rough Al surface lead to rectifying J-V behavior, as opposed to symmetric J-V curves on smooth Al.…”

Section: Sources Of Defects and How To Avoid Themmentioning

confidence: 99%

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Molecules on Si: Electronics with Chemistry

et al. 2009

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Basic scientific interest in using a semiconducting electrode in molecule‐based electronics arises from the rich electrostatic landscape presented by semiconductor interfaces. Technological interest rests on the promise that combining existing semiconductor (primarily Si) electronics with (mostly organic) molecules will result in a whole that is larger than the sum of its parts. Such a hybrid approach appears presently particularly relevant for sensors and photovoltaics. Semiconductors, especially Si, present an important experimental test‐bed for assessing electronic transport behavior of molecules, because they allow varying the critical interface energetics without, to a first approximation, altering the interfacial chemistry. To investigate semiconductor‐molecule electronics we need reproducible, high‐yield preparations of samples that allow reliable and reproducible data collection. Only in that way can we explore how the molecule/electrode interfaces affect or even dictate charge transport, which may then provide a basis for models with predictive power.To consider these issues and questions we will, in this Progress Report, review junctions based on direct bonding of molecules to oxide‐free Si. describe the possible charge transport mechanisms across such interfaces and evaluate in how far they can be quantified. investigate to what extent imperfections in the monolayer are important for transport across the monolayer. revisit the concept of energy levels in such hybrid systems.

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Section: Extracting Insulator Parameters From Current-density-voltagementioning

confidence: 97%

Section: Monolayer Characterizationmentioning

confidence: 99%

Section: Molecular Dipolementioning

confidence: 99%

Section: Quasi-metallic Temperature Behavior For Monolayer-limited Cumentioning

confidence: 99%

Section: Sources Of Defects and How To Avoid Themmentioning

confidence: 99%

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Molecules on Si: Electronics with Chemistry

et al. 2009

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Defect‐tolerant nanoelectronic pattern classifiers

Lee

Likharev

2007

Circuit Theory & Apps

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SUMMARYMixed-signal neuromorphic networks ('CrossNets'), based on hybrid CMOS/nanodevice circuits, may provide unprecedented performance for important pattern classification tasks. The synaptic weights necessary for such tasks may be imported from an external 'precursor' network with either continuous or discrete synaptic weights (in the former case, with the quantization-'clipping'-due to the binary character of the elementary synaptic nanodevices-latching switches.) Alternatively, the weights may be adjusted 'in situ' (inside the CrossNet) using a pseudo-stochastic method, or set-up using a mixed-mode method partly employing external circuitry. Our calculations have shown that CrossNet pattern classifiers, using any of these synaptic weight adjustment methods, may be remarkably resilient. For example, in a CrossNet with synapses in the form of two small square arrays with 4 × 4 nanodevices each, the resulting weight discreteness may have a virtually negligible effect on the classification fidelity, while the fraction of defective devices which affects the performance substantially ranges from ∼20% to as high as 90% (!), depending on the training method.

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Inelastic Electron Tunneling Spectroscopic Analysis of Bias‐Induced Structural Changes in a Solid‐State Protein Junction

Fereiro

Pecht

Sheves

2021

Small

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A central issue in protein electronics is how far the structural stability of the protein is preserved under the very high electrical field that it will experience once a bias voltage is applied. This question is studied on the redox protein Azurin in the solid‐state Au/protein/Au junction by monitoring protein vibrations during current transport under applied bias, up to ≈1 GV m−1, by electrical detection of inelastic electron transport effects. Characteristic vibrational modes, such as CH stretching, amide (NH) bending, and AuS (of the bonds that connect the protein to an Au electrode), are not found to change noticeably up to 1.0 V. At >1.0 V, the NH bending and CH stretching inelastic features have disappeared, while the AuS features persist till ≈2 V, i.e., the proteins remain Au bound. Three possible causes for the disappearance of the NH and CH inelastic features at high bias, namely, i) resonance transport, ii) metallic filament formation, and iii) bond rupture leading to structural changes in the protein are proposed and tested. The results support the last option and indicate that spectrally resolved inelastic features can serve to monitor in operando structural stability of biological macromolecules while they serve as electronic current conduit.

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Control of topography, stress and diffusion at molecule–metal interfaces

Cited by 28 publications

References 27 publications

Molecules on Si: Electronics with Chemistry

Molecules on Si: Electronics with Chemistry

Defect‐tolerant nanoelectronic pattern classifiers

Inelastic Electron Tunneling Spectroscopic Analysis of Bias‐Induced Structural Changes in a Solid‐State Protein Junction

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