Ensuring Reliability Becomes Harder In Multi-Die Assemblies

Multi-die assemblies are bringing together a variety of materials and processes with distinctly different physical properties, creating significant challenges in manufacturing and packaging that can impact yield at time zero and reliability in the field.

What passes electrical screening at the end of the line may look good on paper, but these devices can still fail once exposed to rapid and repeated thermal cycling, mechanical stress, and accelerated aging due to higher utilization, especially in AI data centers. The problems are particularly acute when multiple dies are integrated into the same package, linked together with fine-pitch interconnects. Adhesion breakdown, delamination, stress cracking, and latent electrical defects all can surface after devices leave the factory.

As a result, the industry is shifting from maximizing test pass rates and passing standard reliability testing toward more extensive testing and inspection to ensure packages can withstand many years of service conditions.

“Maintaining planarity and mechanical integrity while going through process thermal cycles is one of the biggest challenges in making heterogeneous materials work together,” said Amit Kumar, senior applications engineer at Brewer Science. “The mismatch in the coefficient of thermal expansion of materials causes stress-related defects at different interfaces and may lead to structural defects within the package.”

This recognition is driving new approaches across materials, inspection, test, process modeling, and design integration.

Materials as the foundation
The foundation of reliability is material science. Adhesives, bonding chemistries, dielectrics, and underfills are being used to support increasingly fine features and aggressive thermal cycles. In heterogeneous integration, where logic, memory, and specialty devices are combined in a single package, mismatches in coefficients of thermal expansion or mechanical strength often determine whether the assembly survives field use.

Early material evaluation is critical. Outgassing, particle generation, and chemical compatibility must be understood well before production volumes ramp. Adhesion layers must maintain integrity across thermal limits, because small reliability failures at the materials level can quickly scale into systemic yield loss.

“Reliability risks don’t show up late in the flow. They start with fundamental material interactions,” said Matt Rich, controls engineering manager at Brewer Science. “If adhesives or dielectrics are not stable under real-world conditions, then downstream inspection and test are simply catching failures that were built in from the start.”

No single material parameter can ensure reliability. Adhesion, stress resistance, and thermal stability all must be balanced simultaneously. Consequently, materials engineers must work more closely with process and packaging teams, breaking down traditional silos and embedding reliability models earlier in the selection and qualification phase.

Advanced packaging stacks now combine low-k dielectrics, polymer adhesives, metal interconnects, and shielding layers. Each of those has different thermal, mechanical, and chemical properties. Managing interactions between these heterogeneous materials requires understanding not just individual material performance, but how they behave as a system under stress.

The tradeoffs in material selection multiply as packaging evolves. Hybrid bonding and stacked die applications require adhesives with high chemical resistance for processing and efficient release mechanisms for rework. Materials must balance high modulus to prevent cracking around edges with enough flexibility to absorb thermal stress. These competing requirements traditionally have forced compromises, but new material systems are being developed to address both needs simultaneously.

The reliability implications extend beyond just mechanical integrity. Outgassing can contaminate sensitive surfaces. Corrosion between dissimilar materials can create electrical failures. Particle generation during processing can seed defects that propagate over time. Each of these issues requires material-level solutions, yet the impacts show up at the system level. Consequently, reliability-driven yield management must start with fundamental material interactions rather than treating materials as passive components in the stack.

Inspection and test evolve toward prediction
In the past, inspection and test were focused on detecting immediate defects. But in advanced packaging, their role is evolving into early warning systems for reliability risks. Engineers still need to know if a device passes today, but they also have to consider whether it will survive years of operation. That changes the expectations for both inspection and test infrastructure.

Inspection is shifting toward reliability detection. Tools are expected to capture defects that may not cause immediate performance issues, but which are precursors to field failures like voids, cracks, or surface non-uniformities that will propagate under stress.

“Reliability risks often begin as benign-looking variations,” said Errol Akomer, applications director at Microtronic. “Hot spots or flash fields might show up as just a different shade during inspection. You’ll have die that fail outright, but then you’ll have compromised die that pass test. These are what we call the walking wounded. Without correlating local inspection data to broader process and test results, you can’t see the patterns that actually predict field failures.”

This correlation challenge drives the need for comprehensive data integration. Test escapes represent a hidden reliability tax. They consume manufacturing resources, pass quality gates, and reach customers before failing. From there, post-production costs cascade from warranty returns, reputation damage, and in critical applications like automotive, they also include safety risks.

Data infrastructure supports long-term tracking
Modern packaging reliability depends on capturing and maintaining process data over extended timeframes. Equipment behavior, process drift, and subtle variations that seem insignificant at the time can become critical when analyzing field failures months or years later.

“You have to store a historical record, a fully contextualized process and machine history for seven years, because one year and three months or shorter is not enough to get the long tail of your equipment failures,” said Boyd Finlay, director of solutions engineering at Tignis, a Cohu Analytics Solution. “You’re always going to have surprise yield events or surprise downtime events if you’re not looking at that data.”

This long-term perspective requires more than simple data storage. The information must be accessible, and it needs to be correlated across process steps, equipment types, and organizational boundaries. Without that continuity, root-cause analysis becomes guesswork.

Data correlation is vital to this shift. Without the ability to track how a die or multi-die device moves from wafer through packaging into test, root cause analysis of field returns becomes nearly impossible. The challenge lies in building systems that can maintain these connections across complex supply chains.

“The challenge is really working with customers to identify where the actual data that tracks the lot movements and splits is stored,” said Aftkhar Aslam, CEO of YieldWerx. “Is it stored in MES systems? In ERP systems? In Excel files? The challenge is to identify a source of the data, import that data, and build the logic to track every die from wafer through packaging and test.”

Once genealogy is established, machine learning models can anticipate which lots are most at risk of reliability excursions. Predictive analytics can reduce retests, improve capacity planning, and flag high-risk lots earlier. The focus is on intervention rather than detection.

However, in practice, the industry’s data silos can slow corrective action. An OSAT may trace reliability issues to a substrate anomaly, but the root cause might lie in fab-side stress conditions. Without access to the full genealogy, manufacturers may wind up chasing symptoms instead of preventing failures. The absence of standardized data flows across supply chain boundaries remains a central challenge to accurate predictions of long-term reliability.

Process modeling shifts the curve left
Reliability cannot be assured just at the package level. It begins with the integrity of the process steps that create the device. Virtual silicon and yield attractor analysis are becoming essential tools to manage reliability upstream. Instead of chasing yield failures one at a time, engineers can model the entire process flow to see how changes ripple across integration steps.

“You make a change in one yield piece, and other things increase, causing something else to go chase in the next iteration,” said Joseph Ervin, managing director of Semiverse products at Lam Research. “The system of doing yield improvement with one or two or three yield failures is very challenging. The value really is shifting the curve left and solving more of the yield challenges simultaneously, and ultimately reaching the highest yield possible.”

The shift toward reliability-first requires rethinking traditional yield metrics. Conventional yield calculations focus on test pass rates at specific checkpoints, like wafer sort, package test, and system-level verification. But these snapshots miss the dynamic nature of reliability. A device that passes electrical test may still harbor latent defects, such as marginal adhesion at a critical interface, micro-voids in underfill, or stress concentrations that will propagate under thermal cycling. These time-dependent failures don’t show up in yield numbers, yet they directly impact the usable output for customers.

“It is the interaction between these multiple steps that gives you the yield you have,” added Ervin. “You can’t rely on one process to solve everything, because all of the steps before are impacting what happens at the next step. Creating models across the whole space is the important piece of making that possible.”

By capturing variability and yield detractors virtually, engineers can identify failure modes that compromise reliability before wafers reach packaging. This reduces costly trial-and-error cycles and embeds reliability margins earlier in the flow.

Design integration prevents systemic risk
Reliability is also dependent on design choices. Multi-die systems raise the stakes for thermal distribution, interconnect density, and partitioning strategies. If those decisions are made without considering packaging constraints, they can lead to reliability risks emerging only after costly prototypes are built. Recognizing this, design teams are now incorporating reliability constraints earlier in the development cycle. When packaging options and die partitioning are being evaluated, reliability models inform those architectural choices, preventing situations where promising designs prove unmanufacturable or unreliable once physical constraints are understood.

“If you have made significant judgment issues in this architecture exploration phase, you will have problems down the road that will create serious challenges for ECOs (engineering change orders) and design iterations,” said Sutirtha Kabir, executive director of R&D at Synopsys. “Architects start with a much broader spectrum of options, and then they have to narrow it down. Without proper exploration, you risk going too far down a path that later proves unworkable.”

Fig. 1: Synopsys’ Kabir discusses design architecture and reliability at SEMICON West. Source: Gregory Haley/Semiconductor Engineering

Embedding packaging reliability constraints upstream reduces the risk of costly late-stage surprises. It aligns design intent with process capabilities and ensures yield is measured in terms of long-term reliability. The economic dimension matters because reliability monitoring requires capital investment in advanced inspection systems, environmental stress testing, and AI-driven analytics. For applications like automotive or high-performance computing, where the cost of a single reliability excursion far outweighs the investment, adoption is accelerating.

The message from SEMICON West was clear — yield in advanced packaging cannot be separated from reliability. Materials set the limits, inspection and test provide monitoring, data delivers correlation, process modeling predicts outcomes, and design integration prevents systemic risk. Each piece of the ecosystem contributes to defining how much of the wafer output can be counted as reliable yield.

Barriers to adoption
Reliability-driven yield management still faces a number of systemic barriers. The most persistent is data ownership. Advanced packaging requires contributions from fabs, OSATs, substrate suppliers, and test houses, each producing data that matters for yield and reliability. Yet data remains fragmented across organizational boundaries. When failures surface downstream, engineers often lack visibility into upstream process histories that would provide a root cause.

Another barrier is validating predictive models. Reliability requires foresight, but most AI models must be trained on past outcomes. Latent defects that only surface after years in the field complicate validation. Without confidence in predictions, manufacturers are reluctant to change process parameters or alter design rules based on model outputs alone.

Drift and explainability are intertwined concerns. AI models may predict high risk of delamination in a specific lot, but without a clear link to physical parameters, engineers hesitate to trust those predictions. Interpretability is becoming as important as accuracy. Black-box algorithms cannot drive decisions in an environment where the cost of acting on a false alarm runs into the millions.

Inspection models face a parallel issue. Algorithms trained on one package geometry may not apply to another. Inconsistent retraining can lead to false alarms, creating yield loss through over-scrapping or excessive rework.

The result is that adoption of reliability-driven yield management remains cautious. Companies deploy predictive analytics in parallel with existing practices, with human approval required before acting on recommendations. This hybrid approach slows progress but reflects the risk calculus of semiconductor manufacturing. Predictions must be proven, not assumed.

Supply chain collaboration
Supply chain collaboration adds another layer of complexity. Material choices underpin reliability, yet their behavior is shaped by interactions with tools, processes, and downstream environments. Adhesives that perform well in isolated testing may falter when paired with specific cleaning chemistries or bonding profiles. Without early cross-supply chain communication, mismatches surface late, leading to reliability excursions and hidden yield loss.

One solution is early-stage alignment. If material providers share stress and adhesion data upstream, and OSATs feed back real-world performance, reliability risks can be modeled before integration. This requires new business models as much as new technology, since participants must balance collaboration against intellectual property concerns.

Process virtualization offers a partial answer. By creating system-level models that capture the interactions between deposition, etch, bonding, and packaging, engineers can simulate reliability outcomes without exposing sensitive recipes. Shared models could allow partners to test compatibility virtually before wafers are committed.

Economic pressures drive change
These material-level and process-level decisions ripple through the entire supply chain, affecting not just immediate yield but long-term reliability economics, which can vary by application. Reliability monitoring requires capital investment in advanced inspection systems, environmental stress testing, and AI-driven analytics. In automotive or high-performance computing, where the cost of a single reliability excursion far outweighs the investment, adoption is moving quickly. In consumer or lower-margin segments, weighing the costs of infrastructure against perceived risk makes manufacturers hesitate.

Trust is the key currency in this shift. Customers expect not only good yield numbers but also confidence that devices will perform in the field. Reliability becomes part of brand value.

The convergence of materials, inspection, data, process models, and design integration points toward a future where reliability and yield are inseparable. Still, the path is incremental. Companies are experimenting with predictive analytics, building pilot twins, and strengthening data links, but broad adoption will take time. The diversity of packaging flows means no single recipe will define reliability-driven yield. Each company must adapt approaches to its own mix of architectures, suppliers, and customers.

Conclusion
At SEMICON West, the emphasis was not on whether reliability should drive yield but on how to make that shift practical. Materials must be qualified with reliability in mind. Inspection and test must evolve into predictive monitors. Data must be shared across boundaries without compromising IP. Models must become interpretable and trustworthy. Each of these shifts is underway, but none is complete.

What unites these efforts is recognition that yield without reliability is meaningless. A package that passes test but fails in the field is not a good product. It’s deferred scrap that escaped detection. Increasingly, true yield must be measured by what survives long-term use under real-world conditions. Reliability, in this sense, is not an adjunct to yield. It defines usable yield. This philosophical shift is driving practical changes. There is more emphasis on stress testing during qualification, tighter correlation between process variation and field returns, and greater willingness to invest in inspection and monitoring capabilities that can detect latent defects before they escape.

The pace of change varies by application and market segment. High-reliability markets like automotive and aerospace are leading adoption, driven by regulatory requirements and the unacceptable costs of field failures. Consumer electronics and mobile applications are moving more cautiously, weighing the costs of additional testing against warranty exposure. Data center and AI applications occupy a middle ground, where reliability directly affects operational costs and customer trust. But across all segments, the trend is clear. Reliability considerations are moving earlier in the development cycle and being integrated more deeply into yield management frameworks.

“Reliability is not a box you check at the end of the flow,” said Brewer’s Rich. “It is the foundation of yield. Every step, from material choices to design architectures to test environments, determines whether yield today translates into usable product tomorrow.”

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