Digital twins dominated discussions at SEMICON West this year, appearing in keynote presentations, panel sessions, and workshops. The conversation reflected a noticeable shift in how the industry views the technology.
What once was mainly associated with design exploration now spans the manufacturing lifecycle. In packaging and assembly, digital twins are emerging as a way to connect design intent with process execution, monitor variations across multiple stages, and in some cases, prescribe corrective actions in real-time.
The pressure to virtualize packaging is mounting. Advanced integration schemes are increasing the number of variables that engineers must manage. Interconnects are tighter, materials are more diverse, and system requirements are more demanding. Even small deviations in planarity, thermal expansion, or warpage can trigger cascading failures. And while such traditional approaches as statistical process control, recipe qualification, and downstream root-cause analysis remain important, they often react too slowly to prevent yield loss once problems arise. Engineers are looking for tools that can anticipate issues earlier and provide actionable choices before damage occurs.
“Customers are demanding heterogeneous integration paths, with multiple die types in a single package,” said Giel Rutten, president and CEO at Amkor. “To maintain yield and reliability, we cannot rely solely on reactive fixes. We must simulate coupling, stress, and thermal interaction up front.”
Fig. 1: Giel Rutten, CEO at Amkor, discusses advanced packaging challenges at Semicon West. Source: Gregory Haley/Semiconductor Engineering
That perspective underscores why the industry is turning to packaging digital twins. Rather than replacing physical testing or inspection, they provide a predictive layer to understand interactions across dies, substrates, and materials before problems manifest in-line.
Test as a feedback engine
Many companies are first experimenting with twins at the package-level test cell. Testing provides an early opportunity to measure packaging performance, but historically, test data was used only for retrospective analysis. Failures were recorded, engineers investigated the causes, and corrective actions arrived after the fact.
A digital twin built at the test cell changes this dynamic. By simulating probe behavior, load board stresses, and package boundaries, it can provide an early signal of developing issues and feed that information back into assembly before yield collapses.
“In our test-cell twin we simulate the probing environment, the load board, and the package boundaries,” said Boyd Finlay, director of solutions engineering at Tignis, a Cohu Analytics Solution. “That lets us detect signal and thermal stress issues in advance, and adjust packaging parameters accordingly.”
This approach transforms test from a pass-fail checkpoint into a feedback mechanism for process optimization. If the model detects rising contact resistance, it can recommend altering bond pressure. If it sees thermal stress building, it can suggest a change in underfill curing conditions. The challenge is speed and accuracy. Models must operate fast enough to match the pace of high-volume test, and they must reliably separate true drift from random noise.
Feed-forward from fab to assembly
Connecting test twins to assembly and packaging is only part of the picture. Many variables that drive packaging failure originate in wafer processing. Variation in deposition thickness, pattern uniformity, or defect density often manifests later as stress or misalignment in assembly. To be credible, packaging twins must absorb feed-forward data from fab and metrology models so they reflect real inputs, not idealized assumptions.
“To make packaging twins credible at scale, you need feed-forward from wafer test to assembly, mapping wafer coordinates through substrates into package,” said Marc Jacobs, senior director of solutions architecture at PDF Solutions. “Otherwise, the twin is blind to upstream variation.”
This need for continuity extends to process models developed at the equipment level. Simulations of etch and deposition are already reducing risk in fabs. Extending those models downstream into packaging provides engineers with realistic expectations of how interconnect layers or surface morphologies will behave once assembled.
“We need to do simulation at multiple scales — system level, board level, package level, chip level, and IP level at different design stages,” said Sudarshan Mallu, senior director of R&D at Ansys, part of Synopsys. “Some chiplets may be fully implemented while others are still at the floor planning or RTL stage. We need methodologies and simulation capabilities that can take in different abstractions and analyze them together.”
Predictive approaches also can leverage sparse metrology combined with physics-based algorithms. Instead of attempting to measure every wafer and every die, which is impractical, these models use limited data points to infer process drift. When passed downstream, that information allows packaging twins to simulate how small upstream deviations could affect assembly outcomes.
“We use sparse metrology and Shockley modeling to infer drift upstream,” said Joe Kwan, director of product management at Siemens EDA. “That information becomes a contextual input for downstream twins, such as packaging or test.”
Fig. 2: Joe Kwan of Siemens EDA shares digital twin presentation at Semicon West. Source: Gregory Haley/Semiconductor Engineering
The larger vision is a chain of interconnected twins that spans from design to fab, packaging, and test. Each node informs the next, creating a more complete picture of how a device evolves from wafer to finished system. The barriers are substantial, however. Aligning data formats, securing intellectual property, and synchronizing models across organizational boundaries requires new frameworks. Yet even partial adoption is proving useful.
When twins become prescriptive
When twins move beyond prediction into prescription, the stakes rise. A diagnostic model may tell engineers that void rates are increasing, but a prescriptive model can run scenarios and propose specific adjustments. In pilot cases, these models already have recommended mid-run parameter changes to salvage product that otherwise would be lost. Engineers remain in control, but the fact that a model can evaluate tradeoffs and suggest the safest option represents a significant step toward closed-loop operation.
“When building predictive models, you basically have two options,” said Gianni Klesse, head of data science and digital business at Digital Solutions Operations at EMD Electronics. “One is to rely on mechanistic models based on physics and chemistry. The second is to rely on AI and machine learning trained on empirical data. In our experience, mechanistic models are almost always infeasible for high-volume chemistry, so machine learning is the engine for our digital twin. If we only deployed a static model, it would eventually drift and become unreliable. That is why robust AI requires continuous monitoring, regular retraining, and rigorous quantification of uncertainty.”
The promise is compelling. Small, timely corrections can save thousands of units from scrap, but the risks are equally clear. If the model is poorly calibrated, its recommendations could exacerbate the problem. If it cannot run quickly enough, its advice may arrive too late to matter. For now, most implementations are advisory, giving engineers a suggested course of action while leaving the decision in human hands. Whether twins will eventually be trusted to act autonomously remains an open question.
Modeling reliability over time
Beyond yield, long-term reliability is emerging as another area where twins may provide value. Conventional reliability testing relies on accelerated stress methods such as thermal cycling, humidity exposure, and electromigration analysis. These tests are slow and costly, and they cannot be conducted inline. Embedding degradation models into packaging twins offers a way to simulate lifetime behavior virtually, providing earlier feedback on potential failure modes.
“We embed models of chemical stability, diffusion, and stress over time into the twin,” said Klesse. “That lets you simulate how an adhesive or encapsulant may degrade months or years out.”
This capability allows engineers to weigh short-term yield against long-term durability, optimizing materials and processes with both in mind. But the reliability domain carries its own challenges. Models rely on scaling accelerated stress data to real-world conditions, and if those scaling laws are off, the predictions will not match field performance. Calibration to actual failure data is essential.
Digital twins also are entering the design space. Multi-die systems present difficult choices in partitioning, interconnect, and power delivery. Decisions made early in design directly affect what happens in assembly. If those choices are made without realistic feedback from manufacturing, engineers can find themselves too far down an impractical path. Using digital twins for architectural exploration can help identify risks before tape-out, giving teams the ability to compare tradeoffs with packaging constraints in mind.
“As we step into multi-die – silicon-based, chiplet-based packaging – many customers are still wondering when to take the dip,” said Sutirtha Kabir, executive director for R&D at Synopsys. “The key drivers are around scaling, reuse, and reducing design time. Without twin-based architecture exploration, you risk going too far down a path that later proves unworkable.”
This reinforces the idea that twins are not limited to production floors. They are part of a broader ecosystem linking design and manufacturing. When data can move both ways, with design informing process choices and manufacturing feeding back into design, the potential for cost savings and time reduction improves significantly.
Barriers to adoption
Several unresolved challenges temper the enthusiasm for digital twins. The first is data trust. Packaging twins require information from wafer processing, assembly, and test, often crossing company boundaries. That raises concerns about intellectual property exposure. Without secure frameworks and clear governance, companies may hesitate to provide the depth of data needed for meaningful models. Even within a single organization, there can be silos that limit integration. A digital twin that only sees partial information risks becoming misleading rather than predictive.
Closely related is the problem of model trust. Engineers will not adopt a digital twin unless its predictions consistently match real-world outcomes. Validation is not a one-time event. Models must be checked against production data often to detect drift. A model that performs well in early trials may diverge as process recipes evolve, materials change, or package types diversify. Continuous recalibration is essential, but it consumes resources and requires rigorous correlation studies.
Another barrier is interpretability. Engineers are unlikely to follow a black-box recommendation without understanding the reasoning behind it. If a model advises increased pressure or altered cure times, teams need to know why. Physics-based twins have the advantage of being grounded in equations that are familiar to engineers, but they may be too slow for real-time use. And while machine learning models run faster, they’re often opaque.
Hybrid approaches are now being explored, but they need to provide explanations that engineers can evaluate. Without interpretability, adoption will stall regardless of the accuracy of predictions.
Latency and computational cost weigh heavily on implementation. Real-time correction only matters if the model can execute quickly enough to keep up with production. High-fidelity physics simulations are notoriously slow. Reduced-order models sacrifice detail for speed, but they risk missing important interactions. Adding AI to the mix can help, but training and maintaining those models add overhead. Packaging twins that run on partial data or approximations may offer some value, but until they can deliver actionable results within the window of high-volume manufacturing, their use will remain limited.
Generality is another concern. Packaging evolves quickly. A model trained on one generation of interposers or material stack may not apply to the next. Engineers worry about the costs of retraining and the risk of overfitting. Transfer learning approaches are being studied, but few have been proven across multiple generations of packaging technology. The industry will need strategies for updating and adapting twins as processes change without having to rebuild them from scratch each time.
“The twin is not just a one-and-done model,” said PDF’s Jacobs. “You need a framework for continuous validation, for drift detection, and for retraining when the underlying process evolves. Without that, what looks like a predictive tool becomes a liability.”
That cautionary view is echoed by design and EDA experts, who say packaging twins will fail without strong data integration. They argue that unless there is agreement on standards for mapping, coordinates, and interfaces, the models will remain fragmented.
“We see the challenge in ensuring consistency across design and manufacturing,” said Synopsys’ Kabir. “The digital twin has to serve as a bridge, not as another silo. Otherwise, you end up with multiple partial views that don’t add up to a complete picture.”
Reliability modeling adds yet another layer of complexity. Stress, diffusion, and degradation mechanisms can be modeled, but only to a degree. Predicting long-term performance from short-term accelerated tests has always been difficult, and embedding those extrapolations into a digital twin raises the stakes. A model that overestimates reliability could allow premature failures to escape into the field. A model that underestimates reliability could lead to overkill and reduced yield. Calibration with field data is essential, but field returns are rare and slow to accumulate.
There are also organizational and cultural hurdles. Many fabs and OSATs are used to protecting their data. Sharing it, even in abstracted form, challenges established practices. Collaboration across EDA, equipment, and assembly companies is necessary, but aligning incentives across those players is far from easy. Industry groups are beginning to discuss standardization, but adoption will require more than technical solutions. It will demand new business models that balance competitive advantage with shared infrastructure.
Where twins go next
Despite these hurdles, interest in digital twins for packaging continues to grow because the benefits are so compelling. A twin that can spot drift before it becomes a failure, recommend a process change mid-run, or highlight a long-term reliability risk could save millions in yield and warranty costs. The possibility of linking design, fab, packaging, and test into a single continuous model remains aspirational, but the trajectory is clear.
The momentum also is being driven by external forces. Market windows are shrinking, and customers expect faster delivery of complex systems with fewer surprises. At the same time, the cost of yield loss is climbing. Scrap and rework at advanced nodes and fine-pitch packaging are significantly more expensive than in past generations, making the economic case for predictive control stronger.
Most engineers prefer advisory systems that provide recommendations, leaving human experts to make the final call. Like the use of autopilot in aviation, advisory systems can reduce workloads and handle routine tasks, but the pilot remains responsible for judgment in unusual situations. Packaging twins may follow the same trajectory, starting as advisory aids and slowly moving into more prescriptive roles as confidence builds.
The push toward digital twins also intersects with larger industry trends in AI, machine learning, and edge computing. As more sensors are added to equipment, and as AI tools improve at handling noisy, multi-dimensional data, the feasibility of real-time models increases. But AI without physics risks becoming brittle, while physics without AI risks becoming too slow. The real value may lie in combining the two, using physics to constrain the model and AI to accelerate its execution. Finding the right balance will be one of the defining challenges for packaging twins in the next decade.
“The challenge is not just building the twin, it’s making it usable in the real environment, with the right balance of accuracy, speed, and interpretability,” said Siemens’ Kwan. “If engineers can’t use it day-to-day, it doesn’t matter how elegant the model is.”
Conclusion
As the technology matures, the scope of digital twins is likely to broaden. What starts as a way to optimize a single process step could evolve into a framework for managing entire assembly lines. Twins could be used to schedule tools, optimize material flows, and even simulate workforce requirements. Reliability twins could become part of customer communication, providing evidence of long-term durability. Design-integrated twins could shorten time-to-market by validating architectures earlier.
But all of this depends on trust in the data, the models, and the ecosystem that supports them. Without that trust, twins risk becoming another layer of complexity rather than a solution. With it, they could transform packaging from a reactive discipline into one that is predictive and prescriptive, aligning design intent with manufacturing reality.
Digital twins are emerging not because they are fashionable, but because the alternative is becoming unmanageable. Complexity is outpacing traditional methods. Packaging has become a critical chokepoint, where small mistakes can cascade into large failures. Engineers need new tools to cope. Digital twins, for all their challenges, represent one of the most promising approaches.
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