Transfer-Learning-Based Virtual Process Optimization for LiPON

Different process parameters have a huge impact on the physical and chemical properties of the LiPON thin films synthesized by magnetron sputtering. It has great significance to model the synthesis process for strengthening the understanding of internal principles and improving the properties of the thin films. Transfer learning can improve model ac⁃ curacy and generalization ability by mining information in historical data sets, so as to better find good process parameters. This paper takes the datasets of LiPON synthesized by magnetron sputtering in literatures as examples to explore the influ⁃ ence of target-substrate distance, sputtering power, and sputtering pressure on the ion-conductivity of LiPON films. Compar⁃ ing with ordinary machine learning, the transfer learning model improves by more than 30% in multiple error metrics. The built model recommended the optimal parameters combination after traversing parameters space, and the predicted ion-con⁃ ductivity of LiPON film is 2.04 μS/cm, which is better than the maximum value in the literature. The mapped contour graph of process parameters and performance recommended for a process parameter range, and the performance of film is good and stable within the range. The analysis of variance and actual samples prove that the method is practical. © 2023 Chinese Institute of Electronics. All rights reserved.