Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis
Analog-mixed-signal (AMS) circuits are highly non-linear and operate on continuous real-world signals, making them far more 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. The approach yields human-interpretable rankings of design knobs and explicit 'what-if' predictions, enabling designers to understand trade-offs in sizing and topology. We evaluate the pipe...
| Authors | Mohyeu Hussain, David Koblah, Reiner Dizon-Paradis et al. |
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| Primary category | cs.AR |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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