Automated Estimation of MBIST Area and Test Time in Heterogeneous Memory IPs via Stacked Ensemble Framework

Abstract

Embedded memories occupy a large portion of modern System-on-Chip (SoC) designs, especially in high-performance applications such as artificial intelligence and edge computing. Memory Built-In Self-Test (MBIST) is commonly used to ensure memory reliability, but it introduces additional area and test time overhead. Accurate early estimation of these overheads is important during design planning, yet conventional methods rely on full Register Transfer Level (RTL) synthesis and test pattern generation, which are slow and resource-intensive. This study proposes a supervised learning framework that predicts MBIST area and test time directly from RTL-level design parameters without synthesis. A dataset of 4,470 samples for area and 624 for test time was generated using Synopsys Design Compiler and MINT, an Intel-enhanced MBIST tool. Input features include memory count, word width, address depth, port configuration, and clock domains. For area prediction, the features are processed through polynomial expansion, log transformation, and scaling, followed by a stacked ensemble model using XGBoost, LightGBM, and a Neural Network with Gradient Boosting as the meta-learner. For test time, XGBoost and LightGBM are combined using Ridge Regression, with hyperparameters tuned through a 100-trial Optuna search. The models achieved 90.68% accuracy for area and 96.80% for test time within a +/-10% margin, improving over baseline methods by 8.53% and 48.80% respectively. The results show that this approach enables faster estimation of MBIST costs and supports more efficient design decisions in memory IP development.