Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis

📰 ArXiv cs.AI

Causal AI framework for AMS circuit design analyzes interpretable parameter effects using a directed-acyclic graph from SPICE simulation data

advanced Published 27 Mar 2026

Action Steps

Discover a directed-acyclic graph (DAG) from SPICE simulation data
Quantify parameter effects using causal inference
Analyze the interpretable results to inform AMS circuit design decisions
Integrate the framework into existing design workflows to improve modeling accuracy and efficiency

Who Needs to Know This

AI engineers and circuit designers on a team can benefit from this framework as it provides a structured approach to modeling complex AMS circuits and understanding the effects of various parameters on their performance

Key Insight

💡 Causal AI can help close the gap between structured design data and real-world performance in AMS circuit design

Key Takeaways

Causal AI framework for AMS circuit design analyzes interpretable parameter effects using a directed-acyclic graph from SPICE simulation data

Full Article

Title: Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis

Abstract:
arXiv:2603.24618v1 Announce Type: cross Abstract: 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

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