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Method for Including Stoichiometric-Dependent Metal Resistivity in Semiconductor Device Modeling

IP.com Disclosure Number: IPCOM000238437D
Publication Date: 2014-Aug-26
Document File: 5 page(s) / 362K

Publishing Venue

The IP.com Prior Art Database

Abstract

A method and system is disclosed for including stoichiometric-dependent metal resistivity in semiconductor device modeling.

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Method for Including Stoichiometric-Dependent Metal Resistivity in Semiconductor Device Modeling

Disclosed is a method for including stoichiometric-dependent metal resistivity in semiconductor device modeling. The method provides an algorithm to integrate effect of materials composition in Technology Computer Aided Design (TCAD) simulator as shown in fig. 1.

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Figure 1

As illustrated in fig. 1, the method uses an analytical expression, while simulating

metal resistance in TCAD simulator to include resistivity as a function of composition. In addition, the method uses a look up table while simulating metal resistance in TCAD simulator to include resistivity as a function of composition. The algorithm also includes temperature dependence, together with composition

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dependence to include in a TCAD simulator where self-heating is important or devices are operated at extreme conditions. The method also provides an algorithm to calibrate analytical expression for composition/temperature dependent resistivity using self-consistent ab-initio numerical simulation.

A generalized algorithm which can be used to compute resistivity for any composite

material, and/or alloy is illustrated in fig. 2.

Figure 2

In an embodiment, the method takes into account a dominant mechanism of carrier scattering, for instance, acoustic phonon versus alloy scattering versus grain boundaries to model the influence of material composition (stoichiometry) on resistivity. Further, the method identifies parameters affected by material composition. Thereafter, the method requires an input from atomistic (ab-initio) simulations. The input is selected from a set of inputs that includes density of states, Fermi energy, effective masses, deformation potential, mass density and sound velocity.

The stoichiometry-dependent resistivity model is given by

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