Genetic programming modelling for the electrical resistivity of Cu–Zn thin films

Abstract

Electrical resistivity measurement is an exact way to find defects in metals and alloys. Defects contribute to the residual resistivity, and determining their number is very important. Defining the inner electrical structure of an alloy is difficult, and especially it is unpredictable in alloys. This article offers a genetic programming formulation to learn how deposition conditions and alloy constituents affect the electrical resistivity of Cu–Zn alloy. Input parameters selected were: measurement temperature (K), Cu and Zn% content in the deposition bath and thin films, bath temperature, deposition potential, and the grain size of the samples. Electrical resistivity values were the output parameters. A total of 130 training and testing sets were selected. The comparative results prove the superior performance in predicting electrical resistivity of the films. The produced model proposes a close relationship for all the input parameters with the electrical resistivity property.