landauer formula
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Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2759
Author(s):  
Jonghwan Lee

A single unified analytical model is presented to predict the shot noise for both the source-to-drain (SD) and the gate tunneling current in sub-10 nm MOSFETs with ultrathin oxide. Based on the Landauer formula, the model is constructed from the sequential tunneling flows associated with number fluctuations. This approach provides the analytical formulation of the shot noise as a function of the applied voltages. The model performs well in predicting the Fano factor for shot noise in the SD and gate tunneling currents.


Biomolecules ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 599 ◽  
Author(s):  
Eszter Papp ◽  
Dávid P. Jelenfi ◽  
Máté T. Veszeli ◽  
Gábor Vattay

Recent electronic transport experiments using metallic contacts attached to proteins identified some “stylized facts”, which contradict conventional wisdom that increasing either the spatial distance between the electrodes or the temperature suppresses conductance exponentially. These include nearly temperature-independent conductance over the protein in the 30 to 300 K range, distance-independent conductance within a single protein in the 1 to 10 nm range and an anomalously large conductance in the 0.1 to 10 nS range. In this paper, we develop a generalization of the low temperature Landauer formula, which can account for the joint effects of tunneling and decoherence and can explain these new experimental findings. We use novel approximations, which greatly simplify the mathematical treatment and allow us to calculate the conductance in terms of a handful macroscopic parameters, instead of the myriads of microscopic parameters describing the details of an atomic level quantum chemical computation. The new approach makes it possible to get predictions for the outcomes of new experiments without relying solely on high performance computing and can distinguish important and unimportant details of the protein structures from the point of view of transport properties.


2019 ◽  
Vol 123 (6) ◽  
Author(s):  
Sergey S. Pershoguba ◽  
Thomas Veness ◽  
Leonid I. Glazman

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