On June 20, DEKRA iST attended the “Inspection/Test Methodologies and Advanced PCBA Manufactureing for AI” conference hosted by SMTA Taiwan Chapter. Michael Peng, the manager of DEKRA iST Interconnection Engineering Department gave a talk on “Reliability Assurance Introduction and Challenges of AI Products.” He provided an overview about how to enhance the stability of AI devices under high-performance computing and diverse complex applications through reliability validation and failure analysis in response to the rapid advancement of AI technology. The conference sparked lively discussion, with Michael Peng engaging deeply with industry, academia, and research experts to exchange a wide range of technical perspectives.
AI Technology and Product Trends
Michael Peng emphasized top five trends in AI technology in the conference, explaining how AI is driving industrial innovation.
- To enhance efficiency and reduce costs, various new training models and techniques are being widely implemented.
- AI can now autonomously handle complex and multitask workflows across various domains of industries and applications.
- Multiple AI systems are working collaboratively, particularly in fields like healthcare and domestic services, which creates greater cooperative potential.
- Voice, image, and structured data are combined and users enjoy more comprehensive and diverse information processing capabilities.
- The rise of edge computing capabilities now enables real-time data processing and analysis at the device level and further improves the timeliness and performance of AI applications.
Michael Peng stressed that data is the most critical asset in technology. Only through effective data collection and utilization can the full potential of AI be realized.
As AI applications expand across diverse fields, from smart manufacturing and healthcare to intelligent homes and vehicles, AI servers have emerged as the critical backbone of these technologies. Unlike traditional servers, AI servers integrate not only CPUs but also high-performance GPUs. The computational power essential for tasks like voice recognition and image processing is critical. Michael further pointed out that PCBs (printed circuit boards) are the operational heart of all electronic components. Unlike modular parts or memory units that can be replaced individually, failure in the PCB typically requires a full-board replacement. As such, the design and manufacturing quality of PCBs directly impacts the reliability and lifespan of AI systems. In the push toward industrial upgrade and reliability-focused engineering, PCB integrity will remain a pivotal factor.
Failure Analysis Process and Steps
Designing AI products requires a comprehensive approach, spanning structure, materials, and packaging techniques, to anticipate hidden risks and ensure stability in real-world use. During the session, Michael underscored that true reliability begins with rigorous, system-level failure analysis. Only by tracing issues to their core can meaningful improvement be made. The discussion was brought to life through real case studies that walked attendees through detailed analysis workflows and techniques.
Once AI chip samples are collected, the analysis begins with non-destructive 3D X-Ray+CT scanning to construct a 3D model. Engineers can then pinpoint internal anomalies and review them with the client to confirm key zones for further inspection. Targeted destructive cross-sectioning follows, where the object is sliced layer by layer to examine material structures and uncover latent defects. Furthermore, comparison and interpretation are performed through high-resolution microscopy and image correlation.
Michael highlighted that surface-level damage rarely reveals the root cause. Accurately pinpointing issues and formulating actionable improvement are only possible when non-destructive and destructive methods are seamlessly integrated into a professional failure analysis process. This integration reinforces the importance of reliability testing and failure analysis in high-end AI applications.
Challenges and Validation Methods of AI Products
With the rapid advancement of AI application, the demand for high-reliability system products is rising accordingly. Michael noted that across the electronics industry, from device selection and PCB layout to SMT process, international standards such as JEDEC and IPC are generally implemented. Standards like J-STD-001 guide soldering process, while IPC-A-610 serves as the reference for visual inspection. These are further complemented by tests for environmental stress, vibration, and tin whisker formation. The implementation of such validation frameworks safeguards product quality, and what’s more, it works to assess the efficacy of process improvement.
Beyond the adoption of standards, both non-destructive and destructive analysis methods play an equally critical role. Techniques such as X Ray imaging, cross-section analysis, and SAT/SAM ultrasonic scanning enable early detection of internal defects, allowing for timely corrective actions to increase yield. Take substrates for example. Leading global manufacturers often impose their requirement for thermal resistance that exceed the IPC standard. These substrates must withstand 8 to 10 reflow cycles without delamination. Moreover, SAT/SAM technology also come into play to enhance inspection accuracy and efficiency.
During the conference, Michael also outlined three common failure mechanisms: tin whiskers, electrochemical migration (ECM), and conductive anodic filament (CAF). CAF originates from internal anomalies within PCB materials, while ECM can occur on any metal interface. Tin whiskers, on the other hand, are metal filaments that grow from pure tin-plated surfaces due to stress. According to the JESD201 standard, highly security-sensitive applications, such as in the military and medical fields, should avoid pure tin-plated devices.
In addition, the surging demand for high-speed computing and big data are driving PCB process toward higher precision. For instance, backdrill stub length design has tightened from ±7 mil to ±2 mil, and registration accuracy has advanced from D+8 to D+4. These shifts significantly raise the bar for process control and, in turn, demand more rigorous reliability validation. Michael recommends adopting the IPC- 9708 standard for thorough validation, from pad design, material selection to reflow profile design. Common issues such as die damage, especially after module-level magnification, can be more efficiently analyzed through AI-assisted image recognition, enhancing both inspection speed and diagnostic accuracy.
Conclusion
The swift rise of AI technologies is redefining the technical threshold across the board. From choosing the right materials and optimizing packaging design to validating processes, every link in the chain carries weight. Michael summed it up: “Data is value. To engineer AI systems that truly deliver on quality and reliability, we must integrate systematic validation at every process node and continuously build a robust foundation of reliability data. This is the only way to identify the best-suited materials and process parameters in the high-frequency, high-density applications.”
DEKRA iST has long been at the forefront of reliability validation and failure analysis, empowering industry partners to identify potential risks and steer continuous optimization. This support is essential in helping AI systems operate reliably under high-load, high-complexity circumstances. Moving forward, DEKRA iST will remain committed to its core value, “Problem Solved,” and closely join hands with the industry to strengthen product quality and boost overall competitiveness!
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