A new formulation for magnetoresistance of electrodeposited CuCoNi alloy films by neural network

Traditional magnetoresistance formulae used to include differences of resistivity of the samples. However, bath conditions and film constituents were not taken into account. Based on the experimental results, a neural network (NN) model-based explicit formulations were developed to predict the magnetoresistance (MR) of electrodeposited CuCoNi alloys in terms of the amount of film constituents, namely concentrations of Ni in the electrolyte (e), ambient temperature (T), R(0) is the resistance without applying a magnetic field, R(B) is the resistance of sample measured under the applied magnetic field, Nickel content at the film % (Ni), Cupper content at the film % (Cu), Cobalt content at the film % (Co), lattice parameter of the films (a), film thickness (t), and magnetoresistance (MR). The test results have revealed that the film compositions were very effective on the magnetoresistance of electrodeposited alloys. Besides, it was found that the model developed by using NN seemed to have a high prediction capability of magnetoresistance of CuCoNi alloys.