Interface roughness, polar optical phonons, and the valley current of a resonant tunneling diode

Lake, Roger; Klimeck, Gerhard; Bowen, R. Chris; Fernando, C.; Moïse, T. S.; Kao, Yung-Chung; Leng, Manhua

doi:10.1006/spmi.1996.0079

Cited by 37 publications

(32 citation statements)

References 11 publications

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“…Extrapolating the valley current to the limit of 0 K shows that 72 % of the valley current tends to be not thermally activated, but is most likely rather related to other elastic and inelastic electron scattering processes, such as interface roughness, alloy, and impurity scattering. [26][27][28] This result suggests that the thermally-activated tunnelling is not as significant in these InGaAs/AlAs/InP RTDs as previously thought. 29 It is important to note, however, that several material characteristics change with temperature, such as conduction band energy, accumulated stress, saturation velocity, and the incoherent scattering rate.…”

Section: Resultssupporting

confidence: 59%

Valley current characterization of high current density resonant tunnelling diodes for terahertz-wave applications

et al. 2017

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We report valley current characterisation of high current density InGaAs/AlAs/InP resonant tunnelling diodes (RTDs) grown by metal-organic vapour phase epitaxy (MOVPE) for THz emission, with a view to investigate the origin of the valley current and optimize device performance. By applying a dual-pass fabrication technique, we are able to measure the RTD I-V characteristic for different perimeter/area ratios, which uniquely allows us to investigate the contribution of leakage current to the valley current and its effect on the PVCR from a single device. Temperature dependent (20 – 300 K) characteristics for a device are critically analysed and the effect of temperature on the maximum extractable power (PMAX) and the negative differential conductance (NDC) of the device is investigated. By performing theoretical modelling, we are able to explore the effect of typical variations in structural composition during the growth process on the tunnelling properties of the device, and hence the device performance.

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Section: Resultssupporting

confidence: 59%

Valley current characterization of high current density resonant tunnelling diodes for terahertz-wave applications

et al. 2017

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“…One was the need to model extended devices through novel boundary conditions [55] which includes quantum charge selfconsistency and strong ineleastic scattering or quasi equilibrium in the contacts. The second key insight is that inelastic scattering from acoustic and polar optical phonons, alloy disorder, and interface roughness in the central device region can be modeled quantitatively in NEGF [56][57][58][59] and compare well to the valley currents in experimental data at low temperatures. At room temperature, however, a very different physics explains the typical high performance, high current density resonant tunneling diode valley current: It is thermionic emission through excited states.…”

Section: Physical Modelsmentioning

confidence: 73%

Advancing nanoelectronic device modeling through peta-scale computing and deployment on nanoHUB

Haley¹,

Lee²,

Luisier³

et al. 2009

J. Phys.: Conf. Ser.

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“…However, even at high temperatures, new islands nucleate on top of existing ones prior to full coalescence of the first layer. As a result, it is difficult to create an atomically-flat surface, which is desirable for making layered devices [42]. Time-varying process conditions may yield smoother interfaces than any constant input.…”

Section: Example: High-dimensional Kmc Simulationmentioning

confidence: 99%

Reduction and identification methods for Markovian control systems, with application to thin film deposition

Gallivan

Murray

2003

Intl J Robust & Nonlinear

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SUMMARYDynamic models of nanometer-scale phenomena often require an explicit consideration of interactions among a large number of atoms or molecules. The corresponding mathematical representation may thus be high dimensional, nonlinear, and stochastic, incompatible with tools in nonlinear control theory that are designed for low-dimensional deterministic equations. We consider here a general class of probabilistic systems that are linear in the state, but whose input enters as a function multiplying the state vector. Model reduction is accomplished by grouping probabilities that evolve together, and truncating states that are unlikely to be accessed. An error bound for this reduction is also derived. A system identification approach that exploits the inherent linearity is then developed, which generates all coefficients in either a full or reduced model. These concepts are then extended to extremely high-dimensional systems, in which kinetic Monte Carlo (KMC) simulations provide the input-output data. This work was motivated by our interest in thin film deposition. We demonstrate the approaches developed in the paper on a KMC simulation of surface evolution during film growth, and use the reduced model to compute optimal temperature profiles that minimize surface roughness.

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Interface roughness, polar optical phonons, and the valley current of a resonant tunneling diode

Cited by 37 publications

References 11 publications

Valley current characterization of high current density resonant tunnelling diodes for terahertz-wave applications

Valley current characterization of high current density resonant tunnelling diodes for terahertz-wave applications

Advancing nanoelectronic device modeling through peta-scale computing and deployment on nanoHUB

Reduction and identification methods for Markovian control systems, with application to thin film deposition

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