Intelligent Analog and Mixed-Signal IC (I-AMS-IC) Design: A Conceptual Framework, and Case Study on AI Integration

A conceptual framework, I-AMS-IC (Intelligent Analog and Mixed-Signal IC), is proposed to enable automation in analog/mixed-signal (AMS) design by integrating machine learning with an emphasis on interpretability and trustworthiness. The framework is demonstrated using a custom-built dataset based on an operational amplifier (Op-Amp) case study. Machine learning models are trained to predict design variables from ten key performance indicators (KPIs). The relationships between design variables and performance metrics are explored using various correlation analysis methods. Among the models tested, XGBoost regression achieves the best performance with a mean absolute percentage error (MAPE) of 16.88%. To enhance interpretability, SHAP (Shapley Additive Explanations) is employed to explain model predictions. The SHAP insights are validated through both correlation analysis and analytical circuit expressions. The study highlights that combining statistical, analytical, and explainability techniques strengthens trust and transparency in AI-assisted AMS design, paving the way for more interpretable and automated design workflows.