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Design of an intelligent PYTHON code to run coupled and license-free finite-element and statistical analysis software for calibration of near-field scanning microwave microscopes

Published

Author(s)

Jeffrey T. Fong, Nathanael A. Heckert, James J. Filliben, Pedro V. Marcal, Samuel Berweger, Thomas M. Wallis, Pavel Kabos

Abstract

To calibrate near-field scanning microwave microscopes (NSMM) for defect detection and characterization in semiconductors, it is common to develop a parametric finite element analysis (FEA) code to guide the microscope user on how to optimize the settings of the instrument to improve its performance. Two problems arise that make the application of the FEA code difficult if not impossible. The first problem is due to the approximate nature of the FEA method and the critical requirement that the accuracy of the FEA solutions be mathematically verified during the entire calibration process. The second problem is a pre- requisite that the user's computer be licensed with the specific FEA software at a sizable cost and training time to the user. In this paper, we solve both problems by designing an intelligent PYTHON code that manages the seamless running of two license-free codes, namely, a compiled parametric COMSOL AC/DC-Module-based code that yields a series of solutions at various finite element mesh densities as input to a FEA-verification code written in a statistical analysis software named DATAPLOT that uses a nonlinear least squares method to check and verify the FEA solution of the COMSOL code. An example of a generic NSMM calibration code running a coupled and license-free finite element and statistical analysis software is presented and discussed.
Proceedings Title
Proceedings of COMSOL Users Conference, Boston, Oct. 2-4. 2019
Conference Dates
October 2-4, 2019
Conference Location
Boston, MA

Keywords

Computational modeling, COMSOL, DATAPLOT, electromagnetics, element type, FEM, finite element method, logistic function, mesh density, near-field scanning microwave microscopy, nonlinear least squares method, PYTHON, statistical analysis, uncertainty quantification.
Created October 2, 2019, Updated May 18, 2020