Helium focused ion beam induced subsurface damage on Si and SiC substrates: Experiments and generative deep neural network modeling via position-dependent input

摘要: Deep neural networks (DNNs) are reshaping many fields due to their end-to-end ability to learn directly from data, leading to outstanding performance, especially in image processing. However, their training requires large image datasets while image preparation can be expensive/time-consuming. If the underlying behavior can be formulated as spatial-dependent, we propose that every pixel from every image can be considered as a different data source, thus enabling a truly large pixel-based dataset with millions of elements (‘position-dependent input’), even when obtained from a few images. The approach is applied to helium focused ion beam nanofabrication, where the cross-section of the helium-damaged region essentially resembles that of a lightbulb, while close inspection reveals the presence of (i) an outer defective region in direct contact with the bulk substrate and (ii) an inner amorphous region filled …