Stochastic Discovery of Variance Mechanisms in Heterogeneous Dielectric Coatings

Microscale spatial and material heterogeneities in 3D printed electrical devices present significant challenges to predictable electrical performance and device reliability. Dielectric particles are often added to dielectric inks to tailor the macro level permittivity of printed dielectric substrates and coatings. However, the combined role of particle morphology, discrete spatial arrangement and material properties on variance is difficult to distinguish experimentally, due to the large parameter space of processing variables and electrical sensitivity to local heterogeneities. We address this challenge by combining a finite element capacitor model with a neural network guided genetic algorithm (GA) to optimize the volume fraction, particle size and permittivity distributions of dielectric particles to identify systems with high capacitance variance. Classification-based machine learning techniques were also applied to the GA-created database to extract correlations between the spatial/material distributions of the dielectric particles and the capacitance variance. Collectively, the study provides a useful framework to correlate electrical performance with both macro- and microstructural variation sources, which is key to accelerating the development of 3D printing materials.