Optimization of (Ba1−xCax)(Ti0.9Sn0.1)O3 ceramics in X-band using Machine Learning

Abstract

Developing efficient electromagnetic interference shielding materials has become significantly important in present times. This paper reports a series of (Ba1−xCax)(Ti0.9Sn0.1)O3 (BCTS) (x=0,0.01,0.05,0.1) ceramics synthesized by conventional method which were studied for electromagnetic interference shielding (EMI) applications in X-band (8–12.4 GHz). EMI shielding properties and all S parameters (S11 & S12) of BCTS ceramic pellets were measured in the frequency range (8–12.4 GHz) using a Vector Network Analyser (VNA). The BCTS ceramic pellets for x = 0.05 showed maximum total effective shielding of 46 dB indicating good shielding behaviour for high-frequency applications. However, the development of lead-free ceramics with different concentrations usually requires iterative experiments resulting in, longer development cycles and higher costs. To address this, we used a machine learning (ML) strategy to predict the EMI shielding for different concentrations and experimentally verify the concentration predicted to give the best EMI shielding. The ML model predicted BCTS ceramics with concentration (x = 0.06, 0.07, 0.08, and 0.09) to have higher shielding values. On experimental verification, a shielding value of 58 dB was obtained for x = 0.08, which was significantly higher than what was obtained experimentally before applying the ML approach. Our results show the potential of using ML in accelerating the process of optimal material development, reducing the need for repeated experimental measures significantly.