AI-enabled Automated PCB Analysis and Reverse Engineering Using Data Augmentation

In the realm of microelectronics, particularly when dealing with printed circuit board assemblies (PCBAs), the challenges of obsolescence and the need for efficient reverse engineering solutions have become increasingly apparent. These challenges resonate across government agencies, including the Department of Defense (DoD), and various industries that heavily rely on microelectronics to ensure the functionality of their systems. Given these shared challenges, the quest for innovative technologies to enhance PCB analysis and re-engineering processes has become imperative. Automation solutions that leverage deep learning are promising but must contend with the limitations of dataset availability. Approaches like data augmentation, which involve the creation of new training examples through transformations and not through creating new data, only offer a partial remedy. The proposed Phase I research intends to explore the viability of creating an AI-driven automated reverse engineering method for PCBAs. Our primary focus is on overcoming the challenge of having a restricted training dataset for deep learning algorithms. Our approach involves generating synthetic X-ray images, which will serve as crucial reference data for training these deep learning algorithms. Additionally, our algorithms will be addressing common X-ray imaging artifacts including beam hardening and scattering, planarity of layers and image distortion and potential motion artifacts during long X-ray scans. The ultimate objective is to equip these algorithms with the capability to autonomously and efficiently segment PCB images and convert them to an open ASCII vector format such as Gerber format that can be used for the production of PCBs. The technique will be initially used on PCBs without components but at the next step will be expanded to the PCBAs that have components on them. The quality and the accuracy of the proposed method will be benchmarked against a complete destructive reverse engineering approach.