Rethinking How Physics is Used in Hardware Design

Modern hardware design is no longer limited by creativity or compute — it’s limited by how and when physics can be evaluated.

In advanced semiconductor and system design, engineers are working at extremes that legacy simulation workflows were never built to handle. A single device can span orders of magnitude in scale, from single-digit nanometer features to centimeter-scale packages, with tightly coupled thermal, mechanical, and electrical behavior. Materials are increasingly heterogeneous, interactions are nonlinear, and performance margins are unforgiving.

Simulation remains the primary tool for understanding these effects, but most teams still treat it as a separate step in the workflow — run, wait, analyze, revise. As system complexity continues to grow, that model hasn’t kept pace. The result isn’t a failure of engineering practice, but a workflow bottleneck: Physical insight arrives too late, too infrequently, and at too high a cost to keep up with the pace of design.

Where the Current Approach Breaks Down

The limitation isn’t computational power — it’s the structure of the workflow itself.

Simulation is inherently episodic. Physical behavior is evaluated only after a model is prepared, meshed, solved, and post-processed. Each step introduces delay and separation from the design process. As designs become more complex, the cost of preparing and running a single simulation ramps up, reducing how often it can be used.

Model preparation is a key constraint. Geometry must be simplified, defeatured, and discretized into meshes that are stable and solvable. These approximations are necessary for numerical methods, but they also distance the analysis from manufacturing reality. At advanced nodes and in tightly integrated systems, small geometric or material details can have first-order effects. However, those are often the first details to be abstracted away.

Multi-physics interactions further compound the problem. Thermal, mechanical, fluid, and electromagnetic effects are typically evaluated in separate tools, with results passed between them in loosely coupled workflows. System-level behavior emerges from the interaction of these domains, but the workflow evaluates them in isolation, often by exporting and re-importing results between tools.

The result is intermittent insight. Engineers make design decisions, pause to run simulations, wait for results, and then revise. As iteration costs increase, fewer iterations are performed. Physical understanding becomes something you sample occasionally instead of something that’s always available while you design.

To compensate, engineers rely on heuristics, guardbands, and overdesign — trading computational efficiency for fidelity to manufacturing reality. As margins tighten and systems become more coupled, that tradeoff becomes increasingly difficult to manage.

The issue isn’t that simulation is inaccurate. Rather, it’s that it can’t be used frequently enough, or at sufficient fidelity, to keep pace with modern design cycles.

From Simulation as a Step to Physics as a Continuous Constraint

If the limitation is that physical insight arrives too late and too infrequently, the question is not how to run simulations faster, but how to change when and how physics is computed.

The alternative is to eliminate simulation as a discrete step. Instead of preparing models, running analyses, and interpreting results in sequence, physical behavior is computed continuously as part of the design process. As geometry, materials, or boundary conditions change, the corresponding physical response updates in real-time, without a separate simulation cycle.

This changes the role of physics in the workflow. Rather than acting as a checkpoint that validates a design after the fact, it becomes a persistent constraint that’s present at every step. Engineers no longer pause to ask what happens when a simulation is run. Instead, they operate within a system where those answers are continuously available.

For this to be viable, two conditions must be met. The computation must operate directly on manufacturing-relevant geometry, without simplification or meshing that removes critical detail. And the results must be deterministic and consistent with established physical solvers. Approaches that rely on approximation alone can’t provide the reliability required for engineering workflows where small errors may propagate into costly failures.

Taken together, this represents a shift from treating physics as an analysis task to treating it as a continuously computed property in the workflow. It expands the range of design decisions that can be evaluated and makes previously impractical tradeoffs routinely accessible during design.

What This Looks Like in Practice

Consider an advanced packaging scenario where thermal and mechanical effects are tightly coupled.

Today, evaluating how power distribution impacts temperature — and how that temperature distribution drives warpage and stress — requires a sequence of steps: prepare geometry, generate meshes, run a thermal analysis, transfer results into a mechanical model, and evaluate deformation. The full cycle can take hours to days, depending on the level of coupling and fidelity required.

Because of this time and cost, only a limited number of design variations are explored. Late-stage issues are often addressed through guardbands or design concessions rather than direct optimization.

In a continuously computed environment, this interaction changes fundamentally. As the engineer adjusts power maps, material properties, or geometry, the resulting temperature distribution and mechanical response update continuously. Warpage, stress, and thermal gradients are no longer outputs of a separate analysis — they’re visible properties of the design as it evolves.

Instead of selecting a design, running an analysis, and waiting for results, engineers can explore a broader range of configurations while maintaining full visibility into physical behavior. Tradeoffs between thermal performance, mechanical integrity, and manufacturability can be evaluated in context, rather than in isolation.

The result is not simply faster analysis, but a different mode of operation, where more design options can be considered and issues are able to be identified earlier in the process.

Toward Continuously Computed Physical Systems

One approach to enabling this shift is to treat physical behavior not as the output of a simulation, but as a continuously computed property embedded directly in the workflow itself.

Vinci is one example of a system designed around this principle. Rather than relying on pre-processing steps such as geometry simplification and meshing, computation operates directly on manufacturing-relevant representations. This allows physical effects to be evaluated without removing the details that often drive real-world behavior.

At its core is a physics foundation model that produces results consistent with established numerical solvers while operating in a form which can be evaluated continuously as inputs change. Unlike surrogate models that approximate specific scenarios, this approach is designed to generalize across geometries, materials, and boundary conditions without requiring retraining on customer-specific data.

When combined with solver-grade consistency and operation at manufacturing resolution, the result isn’t a faster simulation tool, but a different computational layer. It becomes a different way to access solver-consistent physics continuously, without initiating separate analysis workflows.

A Shift in How Physics is Used

As systems become more complex and tightly coupled, the limitations of simulation as a discrete step become increasingly difficult to manage. The issue is no longer computational capacity, but access to physical insight at the pace and fidelity required by modern design.

Systems where physics is computed continuously change how engineers use physics in the design loop. Rather than serving as an intermittent validation step, it becomes a persistent part of the hardware design lifecycle — available at every stage and across interacting domains.

This doesn’t eliminate the role of solvers or experimental validation. It changes when and how they’re used. High-fidelity analysis and measurement remain essential, but they’re no longer the primary means of accessing physical behavior during design.

In that context, simulation evolves. It’s no longer the mechanism through which physics is accessed. It becomes one of several tools used to validate and refine systems whose behavior is already understood from always-on physics computation.

Physics doesn’t disappear from the workflow — it becomes continuously available within it.