Nondestructive sheet resistance prediction of silver nanowire transparent electrode with convolutional neural network

Measuring sheet resistance in metal nanowire films without causing damage is critical for advancing transparent conducting electrode technologies, particularly in emerging applications like flexible electronics, displays, and solar cells. Traditional measurement techniques such as four-point probe and Van der Pauw methods often compromise sample integrity and struggle with accurately capturing the electrical homogeneity of nanowire networks. The non-uniform distribution of nanowires significantly impacts electrical performance, with variations in wire density and junction connectivity leading to inconsistent conductivity and potential device failure. This research paper presents a deep learning technique combining Fast Fourier Transform (FFT)-derived and color metric features to predict the sheet resistance of silver nanowire networks. The inputs for the convolutional neural network (CNN) consist of raw high-resolution optical microscopy images, Fast Fourier Transforms of those images, average color representations, and a combination of all three data types, each processed separately. The combination of image, FFT, and average color data yields the best performance. The predictive capacity of the model extends to assessing non-uniformity in nanowire distribution, a crucial parameter for electronic applications. Thus, the integration of image-derived features provides a powerful tool for material property prediction, enhancing quality control, and advancing materials informatics within nanotechnology and device engineering.