Hardware engineering is inherently unforgiving. Unlike software systems where bugs can be hotfixed in production with minimal friction, a single electrical trace spacing oversight, power distribution bottleneck, or component pinout mismatch can render a physical PCB assembly useless. The financial cost of fabrication re-runs is minor compared to the schedule delays—often stretching to weeks or months—that cripple R&D timelines.
Historically, hardware development has relied heavily on human diligence, static checklists, and downstream peer reviews. In the rush to automate electronic design automation (EDA) workflows with machine learning, a dangerous trend has emerged: relying on the statistical predictions of generative models to propose critical layouts.
We propose a shift in methodology: Evidence-Driven Hardware Engineering.
This document defines the core pillars of a system that rejects statistical heuristics in favor of deterministic physical proofs and audit trails.
1. Why Requirements Matter
Every valid circuit design is a physical manifestation of high-level engineering requirements. When requirements are left as fuzzy natural language paragraphs in PDF datasheets or Word specifications, they cannot be checked programmatically.
In an evidence-driven model, requirements must be structured into machine-readable parameters (e.g., maximum target current, thermal rise limits, and operational voltage bands) from day one. If a design parameter cannot be mapped back to a requirement, it is an orphan variable that introduces risk.
2. Why Assumptions Fail
Most hardware failures do not occur because a router failed to connect a trace. They occur because an unverified assumption survived too long in the design loop. Examples include:
- Assuming a default 1 oz copper thickness can handle a peak 10A current load without thermal breakdown.
- Assuming a regulator pinout matches its alternate sourcing footprint without verifying the manufacturer prefix.
- Assuming a bypass capacitor placement has a short enough return path loop to limit electromagnetic noise.
In our methodology, all assumptions are treated as temporary until verified by physical solvers. A parameter remains flagged as unverified until a mathematical or structural proof is checked.
3. Why Verification Matters
Verification cannot be treated as a post-hoc diagnostic run right before release. It must act as a continuous, active safety gate during the co-design process.
Automated layout proposals must undergo independent audit checking by isolated systems that do not share the layout model’s parameters. This isolation prevents self-referential confirmation bias, ensuring that rule clearances, thermal limits, and pinout checks are executed as hard mathematical limits.
4. Why Traceability Matters
A design state is only as good as its traceability. If a trace width on Layer 2 is 0.45 mm, there must be a traceable pathway explaining why:
- The Requirement: The power plane net must support a peak load of 2.2A.
- The Constraint: Mapped to a maximum temperature rise delta of 10°C.
- The Calculation: Derived using IPC-2152 equations for internal traces.
- The Implementation: Trace routed at 0.45 mm to satisfy the calculation target.
If this chain is broken, the design is un-auditable. Any alteration to the net parameters or layout must automatically invalidate the verification status, forcing a recalculation.
5. Why Evidence Matters
Finally, correctness must be compiled into tangible proof. We define an Evidence Pack (EPK) as an immutable, cryptographically signed package containing the structured requirements, mathematical solvers inputs, DRC pass reports, and human engineering sign-offs.
By treating release readiness as an auditable checklist of digital artifacts rather than a verbal declaration, engineering leads, QA directors, and clients can verify the design’s integrity. Evidence, not promises, is the foundation of high-trust hardware engineering.