Process Modeling

Selberherr, S.

doi:10.1007/978-3-7091-8752-4_3

Analysis and Simulation of Semiconductor Devices 1984

DOI: 10.1007/978-3-7091-8752-4_3

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Process Modeling

S. Selberherr¹

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1989

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Cited by 3 publications

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Pearson versus gaussian effective potentials for quantum-corrected Monte-Carlo simulation

et al. 2006

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We propose a new effective potential for including quantization effects in nanometer scaled nMOSFET. It is calculated as the convolution of a Pearson IV distribution by the Poisson potential resulting from the 2D Poisson equation. Compared to the usual Gaussian distribution, the Pearson IV distribution, calibrated to fit the squared modulus of the ground sub-band Schrödinger's wave function, drastically improves the wave-packet description close to the oxide barrier. This approach has been implemented into a semi-classical Monte-Carlo particle simulator and tested in DGMOS capacitors. We find that the new Pearson Effective Potential gives a very good representation of the electron density profile inside the silicon film, which overcomes the well-known weakness of the Gaussian Effective Potential.

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Pearson versus gaussian effective potentials for quantum-corrected Monte-Carlo simulation

et al. 2006

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Analytical modeling of an ion-implanted silicon MESFET in post-anneal condition

Chattopadhyay

Pal

1989

IEEE Trans. Electron Devices

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A device model for an ion-implanted MESFET with Half-Pearson and Half-Gaussian distribution under post-anneal conditions

Haldar

Maneesha

Khanna

et al. 1994

IEEE Trans. Electron Devices

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Process Modeling

Cited by 3 publications

References 60 publications

Pearson versus gaussian effective potentials for quantum-corrected Monte-Carlo simulation

Pearson versus gaussian effective potentials for quantum-corrected Monte-Carlo simulation

Analytical modeling of an ion-implanted silicon MESFET in post-anneal condition

A device model for an ion-implanted MESFET with Half-Pearson and Half-Gaussian distribution under post-anneal conditions

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