A physics-based scheme for potentials of a-Si:H TFT with symmetric dual gate considering deep Gaussian DOS distribution

Qin, Jian; Yao, Ruohe

doi:10.1016/j.sse.2014.03.009

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…As one may see, the curve of the drain current as a function of gate voltage stretches out as the channel thickness increases. This prediction can also easily be confirmed by the observation in the surface potential calculation for different t si as given previously in Figure 4 in Reference [17].…”

Section: Resultssupporting

confidence: 84%

“…We consider a typical DG n-type intrinsic a-Si:H TFT working under the dual-gate biased mode. The generalized schematic structure of DG a-Si:H TFT has been given in References [4,17,18]. For simplicity's sake, in what follows, we treated the density of trap states throughout the a-Si:H film in a homogeneous way and assumed the field distribution in the active layer to be approximately symmetrical.…”

Section: Approximation Of Potentials and Drain Current Modelmentioning

confidence: 99%

“…Following this, few optimization techniques are needed, which makes the implementation of this scheme more straightforward and suitable for commercial circuit simulators. A more detailed derivation of this approach as well as the related efficiency test can be found in Reference [17].…”

Section: Approximation Of Potentials and Drain Current Modelmentioning

confidence: 99%

“…Partly based on previous work OE16; 17 , in what follows, we present a regional asymptotical scheme for modeling the potentials accounting for the combination of the different kinds of DOS in a-Si:H film. We show the parameters required for accurately describing the I -V characteristics of these devices and how sensitively these parameters affect TFT current.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of current–voltage characteristics for dual-gate amorphous silicon thin-film transistors considering deep Gaussian density-of-state distribution

Qin

Yao

2015

J. Semicond.

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Accounting for the deep Gaussian and tail exponential distribution of the density of states, a physical approximation for potentials of amorphous silicon thin-film transistors using a symmetric dual gate (sDG a-Si:H TFT) has been presented. The proposed scheme provides a complete solution of the potentials at the surface and center of the layer without solving any transcendental equations. A channel current model incorporating features of gate voltage-dependent mobility and coupling factor is derived. We show the parameters required for accurately describing the current-voltage (I -V ) characteristics of DG a-Si:H TFT and just how sensitively these parameters affect TFT current. Particularly, the parameters' dependence on the I -V characteristics with respect to the density of deep state and channel thickness has been investigated in detail. The resulting scheme and model are successively verified through comparison with numerical simulations as well as the available experimental data.

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Section: Resultssupporting

confidence: 84%

Section: Approximation Of Potentials and Drain Current Modelmentioning

confidence: 99%

Section: Approximation Of Potentials and Drain Current Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling of current–voltage characteristics for dual-gate amorphous silicon thin-film transistors considering deep Gaussian density-of-state distribution

Qin

Yao

2015

J. Semicond.

Self Cite

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“…To calculate the surface potential, other methods have been used. For example, very recently, Qin et al developed a novel scheme for surface potential of amorphous silicon TFTs by taking deep Gaussian and tail exponential distribution of the density of states into account [ 81 ]. In Qin et al’s model, the authors adopted Taylor expansion below threshold regime, and the principle of Lamber W function and Schroder series above threshold regime, as well as Chen et al’s model in Section 3.1 .…”

Section: Surface-potential-based Compact Modelsmentioning

confidence: 99%

A Review for Compact Model of Thin-Film Transistors (TFTs)

Jiang

et al. 2018

Micromachines

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Thin-film transistors (TFTs) have grown into a huge industry due to their broad applications in display, radio-frequency identification tags (RFID), logical calculation, etc. In order to bridge the gap between the fabrication process and the circuit design, compact model plays an indispensable role in the development and application of TFTs. The purpose of this review is to provide a theoretical description of compact models of TFTs with different active layers, such as polysilicon, amorphous silicon, organic and In-Ga-Zn-O (IGZO) semiconductors. Special attention is paid to the surface-potential-based compact models of silicon-based TFTs. With the understanding of both the charge transport characteristics and the requirement of TFTs in organic and IGZO TFTs, we have proposed the surface-potential-based compact models and the parameter extraction techniques. The proposed models can provide accurate circuit-level performance prediction and RFID circuit design, and pass the Gummel symmetry test (GST). Finally; the outlook on the compact models of TFTs is briefly discussed.

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Analytical Current–Voltage Model for Double-Gate a-IGZO TFTs With Symmetric Structure for Above Threshold

Hernandez-Barrios

Cerdeira

Estrada

et al. 2020

IEEE Trans. Electron Devices

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A physics-based scheme for potentials of a-Si:H TFT with symmetric dual gate considering deep Gaussian DOS distribution

Cited by 4 publications

References 28 publications

Modeling of current–voltage characteristics for dual-gate amorphous silicon thin-film transistors considering deep Gaussian density-of-state distribution

Modeling of current–voltage characteristics for dual-gate amorphous silicon thin-film transistors considering deep Gaussian density-of-state distribution

A Review for Compact Model of Thin-Film Transistors (TFTs)

Analytical Current–Voltage Model for Double-Gate a-IGZO TFTs With Symmetric Structure for Above Threshold

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