A Production Line Level MTJ Modeling Framework: Integrating Physical Mechanisms, Experimental Data and Manufacturing Variation

We propose for the first time a hybrid neural network-based magnetic tunnel junction (MTJ) modeling framework. By combining graph neural networks (GNN) and deep neural networks (DNN), we implement precise approximation of cross-level coupled physical processes in MTJs, while employing fine-tuning techniques to address discrepancies between experimental data and physical models. The extensive GNN backbone guarantees the scalability for different types of MTJs. Incorporated with stable diffusion (SD) model, this model can accurately predict the MTJ parameter distribution in manufacturing process. Our model framework not only achieves accuracy and scalability but also uniquely reproduces parameter variations in the production line.