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This paper presents an innovative solution to the challenge of part obsolescence in microelectronics, focusing on the semantic segmentation of PCB X-ray images using deep learning. Addressing the scarcity of annotated datasets, we developed a novel method to synthesize X-ray images of PCBs, employing virtual images with predefined geometries and inherent labeling to eliminate the need for manual annotation. Our approach involves creating realistic synthetic images that mimic actual X-ray projections, enhanced by incorporating noise profiles derived from real X-ray images. Two deep learning networks, based on the U-Net architecture with a VGG-16 backbone, were trained exclusively on these synthetic datasets to segment PCB junctions and traces. The results demonstrate the effectiveness of this synthetic data-driven approach, with the networks achieving high Jaccard indices on real PCB X-ray images. This study not only offers a scalable and cost-effective alternative for dataset generation in microelectronics but also highlights the potential of synthetic data in training models for complex image analysis tasks, suggesting broad applications in various domains where data scarcity is a concern. 

Failure analysis and defect detection are crucial processes in industries, governments, and societies to mitigate the risks associated with defective microelectronics. The accurate identification of faulty parts is vital for preventing potential damages. However, traditional manual and automated defect detection approaches face challenges due to the scarcity of ground truth data from defective parts. This limitation hampers the effectiveness of subject matter experts and machine learning models in recognizing and classifying new instances of defects. To address this issue, we propose a synthetic data augmentation workflow that generates virtual defective parts, effectively overcoming the data scarcity problem and enabling the creation of large datasets at a low cost. Our approach enhances defect detection capabilities, empowering industries and governments to improve the quality and reliability of electronic devices. 

Failure analysis of microelectronics is essential to identify the root cause of a device’s failure and prevent future failures. This process often requires removing material from the device sample to reach the region of interest, which can be done through various destructive methods, such as mechanical polishing, chemical etching, focused ion beam milling, and laser machining. Among these, laser machining offers a unique combination of speed, precision, and controllability to achieve a high-throughput, highly targeted material removal. In using lasers for processing of microelectronic samples, a much-desired capability is automated endpointing which is crucial for minimizing manual checks and improving the overall process throughput. In this paper, we propose to integrate laser-induced breakdown spectroscopy (LIBS), as a fast and high-precision material detection and process control means, into an ultrashort pulsed laser machining system, to enable vertical endpointing for sample preparation and failure analysis of microelectronics. The capabilities of the proposed system have been demonstrated through several sample processing examples. 

Abstract Acquiring detailed 3D images of samples is needed for conducting thorough investigations in a wide range of applications. Doing so using nondestructive methods such as X-ray computed tomography (X-ray CT) has resolution limitations. Destructive methods, which work based on consecutive delayering and imaging of the sample, face a tradeoff between throughput and resolution. Using focused ion beam (FIB) for delayering, although high precision, is low throughput. On the other hand, mechanical methods that can offer fast delayering, are low precision and may put the sample integrity at risk. Herein, we propose to use femtosecond laser ablation as a delayering method in combination with optical and confocal microscopy as the imaging technique for performing rapid 3D imaging. The use of confocal microscopy provides several advantages. First, it eliminates the 3D image distortion resulting from non-flat layers, caused by the difference in laser ablation rate of different materials. It further allows layer height variations to be maintained within a small range. Finally, it enables material characterization based on the processing of material ablation rate at different locations. The proposed method is applied on a printed circuit board (PCB), and the results are validated and compared with the X-ray CT image of the PCB part.