When Semiconductor Materials Misbehave

Key Takeaways Material behavior in production depends on the process context that no development environment can fully replicate. In advanced packaging, the interactions that cross domain boundaries are increasingly where failures originate. The most accurate materials data is also the most commercially sensitive, leaving simulation models calibrated against generic inputs rather than production reality. It’s generally assumed advanced materials will behave the same in the lab as in production, but that assumption is now under serious pressure.Typically, the lab result becomes the spec, which then becomes the baseline for qualification. That, in turn, becomes the standard against which field performance is judged. And for most of the industry’s history, this chain of inference held up well enough. Materials were fewer, stacks were simpler, and the interactions between layers were predictable enough that spec-sheet behavior was a reasonable guide to production reality.But as heterogeneous integration evolves from engineering curiosity to the dominant architecture for high-performance computing, the number of materials in a single package has ballooned. The interactions between them are more complex and more consequential, and the environments in which those packages operate are more demanding than the test conditions designed to qualify them.“It’s not like the good old days where, if you have a single die and you know its processes, you can just go to production,” said Mike Kelly, vice president of chiplets and FCBGA integration at Amkor. “Most of these packages are quite complicated mechanically, and certainly electrically. It takes a lot of test field development to get to a point where you’ve got a nice, reliable solution. That can’t be overstated.”What a material does in isolation or in a controlled laboratory sequence is increasingly a poor guide to what it will do when surrounded by dissimilar materials, subjected to multi-step thermal histories, and required to perform reliably over millions of operating hours. The packages now required by advanced AI hardware are mechanically and electrically more complex than those of earlier generations, and the accumulated production experience that once made design decisions straightforward no longer applies as directly. Put simply, the gap between the lab and the fab isn’t new, but it’s getting wider.The complexity problem The most direct explanation for why materials misbehave in production is also the most uncomfortable one to admit. The systems being built today are too complex for anyone to fully model in advance, and the interactions that cause problems are often the ones that no single discipline thought to check.

But the conditions encountered in advanced packaging are not always typical, and a second-order effect in a simple package can become the dominant failure mechanism in a more complex one.“Mechanical stress affects not only reliability, but also changes the electrical parameters of stressed devices and wires,” said Marc Swinnen, director of product marketing at Synopsys. “But mechanical and electrical are rarely considered together. Any simulator is based on fundamental choices as to which effects to include. The problem that arises is that in certain cases a minor effect actually becomes much bigger.”As a result, a package can pass electrical and mechanical simulations, but still fail in production because the interaction between the two effects was never modeled. This is a consequence of the way simulation tools have been developed historically, optimized for specific physics domains by teams whose expertise in adjacent domains is limited. Chip designers are not trained in electromagnetic simulation. Packaging engineers are not trained in static timing analysis. The boundaries between these domains have become the places where the model and the reality most frequently diverge.“Chip, package, and board design are often done separately, but they are significantly linked,” said Swinnen. “This linkage is often buried under generous safety margins that account for the unknown impacts of connecting the chip, package, and board. Safety margins are not free. They bog down performance and increase costs.”The variability problem adds another dimension that simulation handles poorly, even when the physics are correctly specified. A design that performs within spec at a nominal temperature may behave differently when exposed to thermal gradients from a neighboring component. A material rated to a certain mechanical stress limit may encounter stresses during manufacturing assembly that dwarf what it will experience in the field. The combinations of these variables that can occur simultaneously in production are difficult to comprehensively validate even with sophisticated simulation tools.The materials data problem Beneath the simulation challenge is a more fundamental one. The material property values used as inputs to simulations are often wrong, or at least incomplete, in ways that are difficult to correct without data that manufacturers are unwilling to share.The IP problem is one of the central obstacles to closing the gap between simulation and production reality. Simulation tools draw material properties from databases that aggregate published measurements, scientific literature, or foundry-supplied specifications. For well-characterized materials like silicon and copper, those databases are reasonably accurate. For novel materials such as new glass compositions, specialized dielectrics, and proprietary polymer adhesives, the database entries are sparse, sometimes outdated, and occasionally incorrect.

“Simulation tools take some generic property from the internet or from scientific measurement data, or they take foundry-provided data,” said Lang Lin, product management principal at Synopsys. “Whoever is manufacturing has to give or disclose their secret material properties to our simulation tool, and then we can say the simulation result could be well-correlated. Without that, there is no correlation.”The problem is that the most accurate material property data is also the most commercially sensitive. A glass substrate manufacturer that has spent years developing a specific composition and polishing process has no incentive to share the precise mechanical and thermal behavior of that material with the industry at large. The competitive advantage embedded in that data is exactly what justifies the development investment. The result is a structural mismatch. The engineers who most need accurate material data to build reliable simulations are working with the least accurate versions, while the organizations that hold the accurate data have legitimate reasons not to release it.For novel materials at the frontier of what packaging processes aim to do, the problem is even more fundamental. The nonlinear behavior of material properties across different temperatures is well understood for established materials, but it is often less well understood for newer materials.“You have to model the non-linear behaviors of how the mechanical properties of a material change with temperature,” said Lin. “We probably know pure copper well. But for glass with some kind of modified material properties, what will be the temperature dependence? It could be nonlinear in ways we don’t know.”When the field finds what the lab missed The consequences of these modeling gaps show up in production, and sometimes further downstream in field failures that are difficult to trace back to their origin. There is a consistent pattern in how failures reach the field. The dominant cause is rarely a material that failed to meet its nominal specification, but rather a latent defect introduced during manufacturing that the qualification process was never designed to catch.“Many field issues come from latent defects introduced during manufacturing,” said Prasad Dhond, vice president for wire bond and BGA products at Amkor. “Contamination, process variations, and equipment excursions are sources of latent defects that can get exacerbated in the field. In addition to qualification, production control and how you run the factory and the assembly line are very critical.”The difficulty is that latent defects do not always appear as defects at first.