Search for: All records

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. 

Abstract Counterfeit products pose significant economic, security, and health risks. One approach to mitigate these risks involves establishing product provenance by tracing them back to their manufacturing origins. However, current identification methods, such as barcodes and RFIDs, have limitations that make them vulnerable to counterfeiting. Similarly, nonvolatile memories, physically unclonable functions, and emerging techniques like Diamond Unclonable Security Tag and DNA fingerprinting also have their own limitations and challenges. For a traceability solution to gain widespread adoption, it must meet certain criteria, including being inexpensive, unique, immutable, easily readable, standardized, and unclonable. In this paper, we propose a solution that utilizes ultrashort pulsed lasers to create unique, unclonable, and immutable physical tags. These tags can then be read nondestructively using far-field Terahertz (THz) spectroscopy. The primary objective of this paper is to investigate the feasibility of our proposed approach. We aim to assess the ability to distinguish laser marks with varying depths, evaluate the sensitivity of THz reading to laser engraving parameters, examine the capacity to capture high-information-density marks, and explore the ability to capture subsurface tags. By addressing these aspects, our method holds the potential to serve as a universal solution for a wide range of traceability applications. 

As the crisis of confidence and trust in overseas foundries arises, the industry and academic community are paying increasing attention to Printed Circuit Board (PCB) security. PCB, the backbone of any electronic system hardware, always draws attackers’ attention as it carries system and design information. Numerous ways of PCB tampering (e.g., adding/replacing a component, eavesdropping on a trace and bypassing a connection) can lead to more severe problems, such as Intellectual Property (IP) violation, password leaking, the Internet of Things (IoT) attacks or even more. This paper proposes a technique of active self-defense PCB modules with zero performance overhead. Those protection modules will only be activated when the boards are exposed to the attacks. A set of PCBs with proposed protection modules is fabricated and tested to prove the effectiveness and efficiency of the techniques. 

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. 

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.