Analysis of process parameters in the laser deposition of YBa2Cu3O7 superconducting films by using SVR

作者：Cai, C. Z.^*; Xiao, T. T.; Tang, J. L.; Huang, S. J.

来源：Physica C-Superconductivity and Its Applications, 2013, 493: 100-103.

DOI：10.1016/j.physc.2013.03.038

• YBa2Cu3O7 films
• Superconducting transition temperature
• Relative resistance ratio
• Support vector regression
• Modeling and prediction

There are several process parameters in the growth of YBa2Cu3O7 superconducting films by using pulsed laser deposition (PLD). The relationship between the response and process parameters is highly nonlinear and quite complicated. It is very valuable to quantitatively estimate the response under different deposition parameters. In this study, according to an experimental data set on the superconducting transition temperature (T-c) and relative resistance ratio (r(R)) of 17 samples of YBa2Cu3O7 films deposited under various parameters, the support vector regression (SVR) combined with particle swarm optimization (PSO), was proposed to predict the T-c and r(R) for YBa2Cu3O7 films. The prediction performance of SVR was compared with that of multiple regression analysis (MRA) models. The results strongly support that the generalization ability of SVR model consistently surpasses that of MRA via leave-one-out cross validation (LOOCV). The mean absolute percentage errors for T-c and r(R) are 0.37% and 1.51% respectively via LOOCV test of SVR. Sensitivity analysis discovered the most sensitive parameters affecting the T-c and r(R). This study suggests that the established SVR model can be used to accurately foresee the T-c and r(R). And it can be used to optimizing the deposition parameters in the development of YBa2Cu3O7 films via PLD.

• 出版日期2013-10
• 单位重庆大学; 中国工程物理研究院; 中国工程物理研究院激光聚变研究中心