Neural network framework for predicting deposition thickness and electrical resistance in printed electronics

Abstract Screen printing is a widely adopted technique in flexible printed electronics, but accurate control over deposition thickness and electrical resistance remains challenging due to complex interactions among process parameters. This study presents a two-stage neural network-based framework that predicts wet thickness, dry thickness, and electrical resistance from key printing parameters, including mesh count, ink viscosity, squeegee speed, and curing conditions. A Multi-Layer Perceptron (MLP) model, trained on experimentally collected data, achieves high predictive accuracy ( R ² > 0.98) with low mean squared error (MSE), effectively capturing nonlinear dependencies and curing-induced variations. Compared to traditional empirical models, the MLP approach eliminates trial-and-error iterations, reduces material waste, and enhances process reproducibility. The proposed framework enables real-time, data-driven optimization and offers a scalable solution for improving fabrication efficiency in printed electronics.