Non-Volatile Memory Extraction by Deep Learning Methods

Data extraction from large-scale non-volatile memories is a fundamental process in hardware digital forensics that requires extremely high accuracy to minimize manual supervision. Traditional image processing techniques have demonstrated reasonable success, but they are heavily dependent on meticulous sample preparation and image processing. This paper presents an enhanced methodology leveraging the latest advancements in deep learning methods. A comparative analysis is performed between three deep learning models trained with images from mask ROM and OTP fuse memories in 14-nm and 28-nm CMOS technology nodes. The experimental results demonstrate that the proposed approach surpasses in accuracy and offers an almost zero-code versatility.