Paper Title:
Artificial Neural Network Based Process Model for Arsenic Diffusion in Si1-xGex Alloys
  Abstract

The thermal diffusion behavior of ion-implanted Arsenic (As) in SiGe alloy has been investigated and modeled. This paper introduces a neural network based model consisting of physics-based and process-based parameters for evaluating the effective diffusivity of Arsenic through SiGe accurately. The parameters that served as the input to the neural network included Ge content, diffusion temperature and anneal time. The model was validated for the germanium content of up to 45% with the reported data and the existing simulation models in Silvaco. The model incorporates all the effects associated with the change in the process parameters which affect the diffusivity of As in relaxed-SiGe. The model was found to be extremely accurate in predicting the exact dependencies of As diffusivity on physics-based and process parameters. The proposed empirical process model may find suitable application in prediction of thermal diffusion behavior of As in SiGe process-flow with emphasis on reduced computational time.

  Info
Periodical
Advanced Materials Research (Volumes 383-390)
Chapter
Chapter 26: Modeling, Analysis, and Simulation of Manufacturing Processes III
Edited by
Wu Fan
Pages
6800-6805
DOI
10.4028/www.scientific.net/AMR.383-390.6800
Citation
A. A. Sharma, S. S. Mane, "Artificial Neural Network Based Process Model for Arsenic Diffusion in Si1-xGex Alloys", Advanced Materials Research, Vols. 383-390, pp. 6800-6805, 2012
Online since
November 2011
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Price
$32.00
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