VLPred: Accelerated Endurance Prediction Platform enabled by AI Surrogates

We present a fully integrated AI-driven framework for rapid endurance prediction in NVDRAM ferroelectric capacitors. Endurance testing is one of the most time- and resource-intensive steps in memory characterization, often requiring up to 10¹<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> cycles per device. To overcome the scarcity of endurance training data, we propose an experimentally calibrated synthetic data generation pipeline using kinetic Monte Carlo (kMC) simulations in Ginestra™, seeded with experimentally extracted defect parameters. We train a transformer-based AI surrogate using this high-fidelity dataset, achieving an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.992 and enabling ~10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup>x speedup in defect evolution prediction. The surrogate generates large-scale synthetic datasets by sampling initial defect profiles, which are then used to train a hybrid multi-layer perceptron (MLP)-attention model that maps early-life defect characteristics to Weibull endurance distributions. This final endurance prediction model achieves strong agreement with ground truth Weibull parameters, with R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values of >0.98 for η and ~0.9 for β, demonstrating its reliability in capturing endurance distribution characteristics. Wafer-scale prediction of breakdown distributions is demonstrated in one-shot, reducing characterization time by over 10 orders of magnitude. This framework enables scalable, high-throughput reliability screening for ferroelectric memory technologies.