Surface roughness modeling

Publication Type

Conference Proceeding Article

Publication Date

12-2002

Abstract

Microelectronic and molecular devices are formed on the surfaces, which are microscopically rough. To understand how the devices are formed on the rough surfaces and to model their electrical behavior surface modeling has become essential. In this work CAD tool to generate surfaces with roughness has been developed. To represent the surface we have implemented Fast Fourier Transform (FFT), Mandelbrot Weierstrass function, and backpropagation neural networks. FFT method was used because it has been used traditionally for surface modeling. We used the concept of self-similar fractals to model the rough surface (M-W function) because it has been shown that the fractal dimension (D) can quantitatively describe surface microscopic roughness and it is scale independent. We are using Neural Networks to model these surfaces to map the process parameters to roughness parameters.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the International Conference on Scientific and Engineering Computation, Singapore, 2002 December 3-5

First Page

239

Last Page

242

ISBN

1860943454

Identifier

10.1142/9781860949524_0055

Publisher

World Scientific

City or Country

Singapore

Additional URL

https://doi.org/10.1142/9781860949524_0055

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