Resource efficient exploration of ternary phase space to develop multi-layer ceramic capacitors

We demonstrate a fast, efficient combinatorial method for the optimisation of materials for multi-layer ceramic capacitors (MLCCs). Experimentally gathered permittivity-temperature profiles for nine compositions spanning a solid solution are used as input, and with series mixing rules, binary and ternary permittivity contour maps are calculated based on individual layer thicknesses. These are converted into Temperature Coefficient of Capacitance (TCC) contour maps and an algorithm is then used to identify material combinations and individual thicknesses suitable for various MLCC classifications. These facilitate targeted experimentation and allowed experimental verification of the methodology. The approach highlights that binary systems can achieve X9 (R, S T and U). classification but the addition of a third complimentary material can facilitate a tighter TCC classification (X9P) with a wider tolerance in layer thicknesses, providing a better strategy for mass production of MLCCs. The room temperature permittivity (ƐRT) for combinations with similar TCC values can also be evaluated to ensure adequate ƐRT is achieved for commercial applications.