Today’s large language models are extremely good at appearing confident. They compile sentences, suggest code snippets, and structure documentation with an air of authority. But when applied to hardware engineering, statistical confidence is a high-risk liability.
In software, a bug in a web layout can be hotfixed in production. In hardware, a trace clearance conflict or thermal via error is a $10,000 board recall and a two-month fabrication delay. Physical systems have no hotfixes.
The Hallucination Problem in EDA
When an AI model suggests a voltage regulator layout or high-speed differential signal trace, it acts as a recommendation engine. It suggests parameters based on patterns it has seen. But AI models have no concept of physical laws:
- They do not know if a trace will experience cross-talk coupling.
- They do not evaluate current density loops.
- They do not calculate heat conduction variables.
If an engineer trusts a model’s layout proposal without verifiable verification, they are design-building on assumptions.
Moving From Confidence to Evidence
To build high-trust hardware, co-design tools must shift their focus. We do not need AI models to say “I am 95% confident this PCB layout is correct.” We need systems that prove correctness:
- Deterministic Execution: Every parameter propuesta (current capacity, path thickness, heat sinks) must be delegated to hardcoded math libraries.
- Explicit Verification: Designs must undergo verification audits against established safety guidelines (e.g., IPC-2152 temperature limits).
- Auditable Artifacts: The complete audit log — containing parameters, solver inputs, rules passes, and checksums — must be packaged as digital evidence.
At OmeraCode, this digital evidence is compiled into an Evidence Pack. It acts as a safety gate. We propose options using AI, calculate using math, verify using rules, and lock the final release with the engineer’s cryptographic seal.
Rigor, not speculation, is the path forward for hardware automation.