AI RESEARCH
Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis
arXiv CS.AI
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ArXi:2603.24618v1 Announce Type: cross Analog-mixed-signal (AMS) circuits are highly non-linear and operate on continuous real-world signals, making them far difficult to model with data-driven AI than digital blocks. To close the gap between structured design data (device dimensions, bias voltages, etc.) and real-world performance, we propose a causal-inference framework that first discovers a directed-acyclic graph (DAG) from SPICE simulation data and then quantifies parameter impact through Average Treatment Effect (ATE) estimation.