Unlocking the Power of Physics of Failure Models: The Building Blocks of Reliability Assessment

In last week's introduction to Physics of Failure (PoF), I shared my excitement about this revolutionary approach to reliability engineering. Now, let's dive deeper into the heart of PoF: the models that serve as our crystal ball for predicting and preventing product failures.

As engineers, we're always looking for ways to better understand and quantify the world around us. PoF models are our tools for doing just that in the realm of product reliability. These models aren't just abstract mathematical constructs; they're our way of translating real-world physics into actionable insights.

At their core, PoF models are mathematical representations of the physical processes that lead to failure. They take into account factors like material properties, environmental conditions, and applied stresses to predict how and when a product might fail. What makes these models so powerful is their basis in fundamental scientific principles rather than just historical failure data.

Let's look at a few key types of PoF models:

1. Fatigue Models: These are crucial for predicting failures due to repeated stress. The Coffin-Manson model, for instance, is widely used for low-cycle fatigue in metals. It relates the number of cycles to failure to the plastic strain amplitude. I've used this model countless times when dealing with solder joint reliability in electronic assemblies.
2. Corrosion Models: For products exposed to harsh environments, corrosion models like the Peck model are invaluable. This model considers both temperature and humidity to predict corrosion-induced failures. I remember applying this model to improve the design of outdoor electronic enclosures, significantly extending their lifespan in humid coastal environments.
3. Electromigration Models: In the world of microelectronics, Black's equation is a go-to model for predicting failures due to electromigration. It considers current density and temperature, allowing us to design more reliable integrated circuits.
4. Time-Dependent Dielectric Breakdown (TDDB) Models: These are crucial for assessing the reliability of gate oxides in semiconductors. The E-model and 1/E model are commonly used, helping us predict the lifespan of critical components in everything from smartphones to satellites.

What's fascinating about these models is how they can be combined and adapted to tackle complex, multi-physics problems. In my work with multi-layer ceramic capacitors (MLCCs), we often use a combination of thermal stress models and electrochemical migration models to predict failure under various conditions.

One of the most exciting developments I've seen in recent years is the integration of these PoF models with advanced simulation techniques. Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) allow us to apply these models to complex geometries and systems, providing unprecedented insights into potential failure modes.

However, it's important to note that these models aren't infallible. They require careful calibration and validation against experimental data. I've learned the hard way that blindly applying a model without understanding its limitations can lead to misleading results. That's why I always stress the importance of combining PoF modeling with targeted testing and real-world data collection.

The beauty of PoF models lies in their predictive power. Unlike traditional reliability methods that often require extensive historical data, PoF models allow us to make informed predictions about new designs and technologies. This is particularly crucial in today's fast-paced innovation landscape, where we often don't have the luxury of years of field data before launching a product.

As we continue to push the boundaries of technology, these models are evolving too. Machine learning and AI are being integrated with PoF models to create hybrid approaches that combine the best of data-driven and physics-based methods. It's an exciting time to be in this field!

In next week's installment, we'll explore how we take these component-level models and scale them up to assess system-level reliability. We'll delve into the concept of Failure-Free Operating Periods (FFOP) and how we aggregate these to understand the reliability of complex systems.

Until then, I encourage you to think about the products you use every day. What physical processes might be at work, slowly leading to their eventual failure? And how might understanding these processes help us design better, more reliable products for the future?

References:

Dasgupta, A., & Pecht, M. (1991). Material failure mechanisms and damage models. IEEE Transactions on Reliability, 40(5), 531-536.

Pecht, M. G., & Nash, F. R. (1994). Predicting the reliability of electronic equipment. Proceedings of the IEEE, 82(7), 992-1004.

Suhir, E. (2002). Accelerated life testing (ALT) in microelectronics and photonics: Its role, attributes, challenges, pitfalls, and interaction with qualification tests. Journal of Electronic Packaging, 124(3), 281-291.

Vichare, N., & Pecht, M. (2006). Prognostics and health management of electronics. IEEE transactions on components and packaging technologies, 29(1), 222-229.

Further Reading: For a more detailed exploration of this topic, check out the extended version of this article on Medium: Unlocking the Power of Physics of Failure Models: The Building Blocks of Reliability Assessment"