[2603.24618] Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis

Computer Science > Hardware Architecture arXiv:2603.24618 (cs) [Submitted on 24 Mar 2026] Title:Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis Authors:Mohyeu Hussain, David Koblah, Reiner Dizon-Paradis, Domenic Forte View a PDF of the paper titled Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis, by Mohyeu Hussain and 3 other authors View PDF HTML (experimental) 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 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 pipeline on three operational-amplifier families (OTA, telescopic, and folded-cascode) implemented in TSMC 65nm and benchmark it against a baseline neural-network (NN) regressor. Across all circuits the causal model reproduces simulation-based ATEs with an average absolute error of less than 25%, whereas the neural network deviates by more than 80% and frequently predicts t...