Large Language Models are transforming Electronic Design Automation, but feeding a raw netlist to an AI model cannot deliver reliable verification results.
A netlist describes what is connected, not why. Design intent, power domains, operating modes, and system constraints are essential to understanding circuit behavior, yet none of this information is explicitly captured in the netlist itself.
This white paper explains why context is the foundation of trustworthy AI for analog and mixed-signal verification. It presents Aniah’s approach based on deterministic semantic reconstruction, root-cause clusterization, and System Conditional Analysis (SysCon) to transform raw circuit data into a verified, causally grounded model.
We explain how Aniah OneCheck® reconstructs the semantic context required for meaningful analysis and how Amigo®, Aniah’s AI assistant, leverages this verified model to provide explainable, auditable, and actionable insights.
At Aniah, we believe that AI should not replace verification it should make verification more understandable, more interactive, and more efficient. Because in analog design, context isn’t optional. It’s foundational.
