Simulation Closes Gap Between Chip Design Optimization And Manufacturability

Simulation is playing an increasingly critical and central role throughout the design-through-manufacturing flow, fusing together everything from design to manufacturing and test in order to reduce the number and cost of silicon respins.

The sheer density of modern chips, combined with advanced packaging techniques like 3D stacking and heterogeneous integration, has made iterative physical prototyping too expensive and time-consuming. Errors often slip through in the early phases of design, which can lead to multiple silicon respins, yield losses, and delayed product launches. Simulation can help shrink the number of errors early in the flow, speeding up time to market, reducing defectivity, and improving overall efficiency.

In the past, chipmakers relied on pre-silicon validation to ensure digital circuits functioned as intended, but that’s no longer sufficient for advanced ICs and packaging. Today’s semiconductor architectures incorporate multi-die integration, finer interconnect pitches, and higher thermal and power densities, so simulation must extend beyond electrical performance.

“The complexity of today’s devices requires a multi-physics approach,” says John Ferguson, product management director of Calibre nmDRC applications at Siemens EDA. “You’re not just modeling electrical behavior. You have to account for thermal expansion, mechanical stress, and high-speed signal propagation across multiple material interfaces.”

Simulation is part of a growing arsenal of tools that includes predictive models, digital twins, and AI-driven analyses to assess signal integrity, power distribution, circuit aging, and mechanical stress well before silicon is fabricated. But as simulation takes on a larger and more central role, expanding from a single chip to complex and increasingly heterogeneous systems, the challenge is ensuring that an abstracted view accurately reflects real-world conditions, realistic workloads, and manufacturing variability. In effect, it needs to bridge the gap between theoretical predictions and practical performance with increasing levels of accuracy while also dealing with more customization and a slew of complex interactions, not all of which are obvious early in the flow.

Increasing dependence on simulation
The shift toward more complex simulations has improved design confidence, but there’s more to it than just achieving absolute accuracy. Perfection is important, but so is optimization, and this is an area where simulation is really starting to have an impact.

“Even if a simulation result is not 100% accurate in an absolute sense, it still provides an invaluable guide for architectural optimization,” says Marc Swinnen, director of product marketing at Ansys. “When one design choice significantly outperforms another in simulation, it helps engineers understand which modifications will yield the best real-world results.”

Simulation is an extremely valuable directional tool, guiding engineers toward the most robust, efficient, and manufacturable designs. That gives engineers the ability to weigh tradeoffs, identify potential failures before fabrication, and refine designs without costly, time-consuming silicon spins. The goal is to get as close as possible to first-pass yield before the first prototype ever hits the lab. From there, everything can be verified with real-world testing.

“You don’t get to trust a simulation until you’ve correlated it to enough real-world test data,” says Nitza Basoco, technology and marketing director at Teradyne. “The process is iterative. Initial predictions will always have gaps, and test results must continuously feed back into refining the model.”

This iterative loop is particularly important in advanced packaging and RF applications, where factors such as warpage, interconnect reliability, and process variations can have a significant impact on device performance.

“Modeling semiconductor physics accurately is difficult because most models rely on electrical characteristics such as IV curves and S-parameters,” explains Murthy Upmaka, system solutions engineering fellow at Keysight. “However, accurately modeling nonlinear phenomena remains a challenge due to inherent limitations in behavioral models, which can lead to discrepancies between simulations and real-world measurements.”

The challenge lies in closing this simulation gap — ensuring that simulated models continuously evolve alongside real-world manufacturing data to improve predictive accuracy over time. This requires tighter integration between simulation and test.

“You run initial simulations under nominal conditions — nominal voltage, nominal process corner, etc.,” Basoco explains. “But once you start introducing variations like temperature fluctuations, process shifts, voltage changes and different environmental stresses, discrepancies emerge, and that’s where real-world test data becomes essential.”

Parametric coverage
Even the most rigorous physical testing has limitations. While engineers can run a device through various voltage, temperature, and frequency conditions, they still are constrained by time, cost, and equipment availability. This is where simulation offers a distinct advantage, because it enables engineers to explore the entire parametric space of a device’s performance.

“No matter how extensive a physical test, it is only valid for that specific item and cannot account for all possible manufacturing tolerances and their combinations, or all possible pressures and temperatures and load combinations across all frequencies,” says Ansys’ Swinnen. “Testing the entire parametric space that impacts device behavior is impractical in real-world testing, but is a natural product of simulation. The robustness and reliability of a product is greatly enhanced through simulation in a way that is very expensive and time-consuming to replicate by testing actual devices.”

The ability to explore the full range of operating conditions, even those that might not be physically tested, provides engineers with a statistical understanding of how variations in process, materials, and environment will impact performance. This is particularly critical in high-frequency RF designs, where even minor variations in layout, parasitics, or impedance mismatches can drastically alter real-world behavior. Similarly, advanced power devices must withstand a wide range of thermal and electrical stress scenarios, many of which are difficult or impractical to replicate exhaustively in a lab.

“When modeling and simulating the behavior of a very high-frequency system, even minor changes may impact the results,” says Quaid Joher, director of engineering, SI/PI, at Advantest. “A high-frequency design cannot be built on the ‘rule of thumb’ anymore. Proper modeling and simulations are required to predict the right outcome.”

Simulation allows engineers to map out full tolerance stack analyses through parametric sweeps, Monte Carlo simulations, and AI-driven optimizations, reducing risk and avoiding costly redesigns down the road. Instead of relying solely on trial-and-error prototyping, designers can anticipate worst-case scenarios and build robustness into their designs from the outset.

For instance, time-domain reflectometry (TDR) simulations provide valuable insight into impedance mismatches in GHz-to-THz-range designs, an area where physical measurement equipment like network analyzers becomes prohibitively expensive.

“With simulation tools, parametric sweeps of variations can map out full tolerance stack analyses before fabrication, offering engineers insights that would otherwise require costly hardware re-spins and could lead to extended lead-times and missed delivery targets,” adds Joher. “Likewise, manufacturing tolerances, such as changes in feature size, location, and thickness, can predict outcomes that impact yield. This approach is used to optimize design and to show where systems may experience yield losses due to incorrectly manufactured features.”

As heterogeneous integration and advanced packaging continue to evolve, the need for broad parametric exploration will only grow. Simulation is no longer just about predicting performance. It now includes designing for resilience, ensuring that manufacturing tolerances, process shifts, and environmental variations don’t create unexpected failures down the line.

“Having early exploration, the ability to prototype, and making early technology decisions is critical to the success of these more complex multi-die designs,” says Keith Lanier, product management director at Synopsys. “You need to analyze power delivery, thermal integrity, and interconnect structures well before fabrication. The challenge is ensuring that early models accurately reflect real-world performance.”

Key challenges in simulation accuracy
Despite their growing importance, simulations are not infallible. While they can model electrical performance under normal conditions, they often struggle to predict the full impact of high-frequency interactions, material inconsistencies, and process variability:

• High-frequency behavior mismatches: At mmWave and terahertz frequencies, impedance mismatches, unexpected signal attenuation, and intermodulation distortion can significantly diverge from simulation predictions.
• Material inconsistencies: Variability in substrate properties, interconnect resistance, and dielectric properties can introduce deviations that are difficult to model accurately.
• Manufacturing process variations: Bond pad misalignment, warpage, and stress-induced defects can alter device performance in ways that are often underestimated in pre-silicon validation.

One of the most persistent gaps in simulation accuracy is the integration of parasitics, coupling models, and system-level interactions. While simulations provide a theoretical framework for device behavior, they often fail to capture the cumulative effects of PCB parasitics, substrate losses, and package-induced distortions that can significantly alter real-world performance.

“The losses at high frequencies grow significantly, so array antennas must be carefully designed to maintain gain and minimize power dissipation,” says Keysight’s Upmaka. “The challenge in simulation is integrating PCB parasitics, substrate effects, and coupling models into the system-level analysis.”

Thermal modeling presents another major limitation. As multi-die stacking and ultra-dense integration become more common, thermal dissipation and mechanical stress introduce failure points that cannot always be anticipated in simulation. While modern tools provide baseline estimates, they often fail to fully capture how thermal expansion mismatches and stress accumulations affect device longevity.

“Thermal effects in 3D-stacked devices create stress mismatches between different material layers,” says Matt Grange, senior product engineer for Siemens EDA. “Relying on simulations in just one domain fails to capture the full mechanical impacts, which require complete multiphysics analyses and real-world testing for verification.”

Beyond signal integrity and thermal issues, variations in wafer thinning, bump co-planarity, and underfill application remain difficult to predict. These seemingly minor deviations can accumulate, affecting overall yield and long-term reliability.

“No matter how sophisticated the package interconnect design model and inspection defect model, process variations affect real-world package interconnect performance in ways these models don’t always comprehend,” says Jack Lewis, CTO at Modus Test. “That’s why high-volume electrical test data on the package interconnect junctions at all stack-up levels is critical to train and refine the models to account for mechanical process variations that wouldn’t be obvious in a purely simulated environment.”

Furthermore, advanced packaging introduces finer interconnects, heterogeneous integration issues, and additional sources of mechanical and electrical stress. Even minor deviations in thermal expansion coefficients, electrical parasitics, or material adhesion can accumulate, leading to unexpected failures in final devices. These discrepancies highlight the need for continuous calibration and refinement of simulation models, ensuring they evolve alongside real-world manufacturing conditions.

“As you add multiple chiplets and other materials, manufacturing introduces warping, thermal expansion mismatches, and stress accumulation that affect performance,” says Siemens EDA’s Ferguson. “If the package is warped, how do you make the correct connections on the test bench? Are you actually getting the signal across all the different chiplets as expected?”

AI/ML-enhanced accuracy
As semiconductor complexity grows, traditional physics-based simulation tools struggle to account for real-world variability. Fixed models and predefined assumptions often fail to capture subtle material inconsistencies, process fluctuations, and high-frequency effects that impact actual device performance. To bridge this gap, engineers increasingly are turning to AI and machine learning (ML) to refine simulation accuracy by integrating real-world test data, process feedback loops, and predictive failure analysis.

“AI and ML are accelerating simulation workflows by automating optimization tasks that previously required manual intervention,” says Advantest’s Joher. “These tools can identify correlations between material properties, process variations, and test outcomes, making it possible to refine models dynamically.”

One of AI’s greatest advantages is its ability to continuously refine models based on empirical feedback. Rather than relying on static simulations with best-guess tolerances, AI-enhanced tools can dynamically incorporate material inconsistencies, temperature fluctuations, and stress-induced failures into simulation workflows. This enables engineers to adapt simulations in real time, reducing discrepancies between theoretical predictions and actual silicon behavior.

“With the size of these dies, the number of interconnects, and the sheer design complexity, AI solutions are becoming necessary for managing optimization,” says Synopsys’ Lanier. “It’s no longer just about modeling individual factors like power delivery or signal integrity in isolation –  you need to consider the entire system holistically and iterate rapidly to close the gap between predicted and actual performance.”

Despite these advantages, AI-driven simulations still are vulnerable to over-fitting models to idealized conditions. In semiconductor design, overfitting occurs when a simulation aligns too closely with predefined assumptions and fails to account for real-world variability. This creates overly optimistic performance predictions, particularly in high-frequency designs where parasitic effects, thermal gradients, and mechanical stress can introduce failures that are not reflected in pre-silicon validation.

“Even with AI-enhanced modeling for non-electrical package inspection, it’s important to remember that the model is still only a prediction with minimal feedback until it causes actual yield loss,” says Modus Test’s Lewis. “What AI brings to the table is the ability to compare millions of high precision electrical interconnect test results against model simulations and inspection results, refining accuracy of both the package simulation and the defect inspection model in ways that weren’t possible before.”

Additionally, many traditional simulation models assume static material properties, ideal electrical contacts, and uniform thermal distribution, but these assumptions do not always hold in heterogeneous integration and advanced packaging. For example, in 3D-stacked devices, variations in thermal expansion coefficients between die and interconnect layers can induce mechanical stress, altering electrical performance in unpredictable ways — an effect that may not be fully captured in pre-silicon models.

“We are getting into more complexity and higher speeds, and every aspect is important. Designers are forced to pay attention to every detail,” adds Joher. “As computational power rapidly increases, true 3D precision modeling will become a ‘must’ in most, if not all, components.”

AI-driven simulations help bridge this gap by continuously updating thermal, electrical, and mechanical parameters based on real-world data, ensuring that predictive models reflect actual manufacturing tolerances.

“Thermal effects in 3D-stacked devices create stress mismatches between different material layers,” adds Siemens’ Grange. “Relying on simulations in just one domain can provide baseline estimates, but they often fail to capture the full mechanical impact. AI-driven multiphysics analysis helps bridge this gap, refining models with real-world process data.”

Beyond physics-based predictions, AI improves defect detection and performance drift analysis — areas where traditional simulations often fall short. While conventional simulation methods predict idealized performance under controlled conditions, AI can analyze large datasets from high-volume testing to identify early-stage reliability risks and process deviations. This is particularly valuable in advanced packaging and heterogeneous integration, where failures may emerge gradually rather than appearing as immediate defects.

“AI is helping us shift from a reactive model to a predictive one,” says Teradyne’s Basoco. “We can now identify drift and anomalies across massive data sets before they translate into real-world failures. This is an invaluable step toward improving yield and reducing costly respins.”

The future of simulation and validation
Looking ahead, AI and ML will play an increasingly central role in bridging the gap between simulation and real-world test data. The ability to integrate live metrology data into simulation workflows will drive greater predictive accuracy, reducing reliance on costly fabrication iterations.

“The integration of AI into semiconductor simulation is not just about improving accuracy,” says Joher. “It’s about enabling real-time adaptability. By incorporating live process data, AI-driven models can evolve alongside manufacturing conditions, ensuring continuous improvement.”

Such real-time feedback also enables predictability. “AI-driven simulation is changing the game by making predictive modeling more dynamic,” says Keysight’s Upmaka. “The ability to adjust parameters based on real-time feedback means engineers can address process variations before they become yield killers.”

Ultimately, the key to closing the simulation gap lies in a hybrid approach — one that leverages AI to enhance predictive models while maintaining a strong connection to empirical validation. By integrating simulation, test, and AI-driven process refinement, semiconductor engineers will be able to develop more resilient designs, optimize for manufacturability, and significantly reduce time-to-market for next-generation devices. The road ahead is not about choosing between simulation and test, but about making them work together more effectively than ever before.

Conclusion
The semiconductor industry is rapidly shifting toward a data-driven design and test model, where simulations are no longer static approximations but continuously evolving predictive engines. AI, real-time metrology, and production analytics are blurring the lines between simulation and test, allowing engineers to validate designs faster and with greater confidence.

“At the end of the day, no simulation is perfect,” adds Teradyne’s Basoco. “The key is closing the gap, making sure that test data refines our models over time so that next-generation designs get smarter, more efficient, and more manufacturable from the start.”

With the continued integration of AI, machine learning, and real-time process feedback, the semiconductor industry is moving toward a new era of design validation, where the boundaries between simulation and physical testing blur. The result will be more predictive, adaptive, and reliable semiconductor development cycles.

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