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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 10¹² 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 R² of 0.992 and enabling ~105x 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 R² 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. 

While negative capacitance (NC) has been demonstrated in ferroelectric-dielectric (FE-DE) heterostructures in the form of capacitance enhancement, all experimental evidence, to date, suggests the existence of domains therein. Here, we address the question: what are the conditions to achieve ideal, domain-free NC in FE-DE heterostructures? Our main claim is that for given thicknesses of the ferroelectric and the dielectric layers, there is a critical value of domain wall energy parameter— above which the system would be stabilized in an ideal and robust domain-free NC state and would be robust against domain formation. Our analyses suggest that to achieve ideal NC, efforts should lie in understanding the means to control the domain wall energy on all fronts, both theory and experiments via high throughput design, discovery, and engineering of ferroelectrics. 

Abstract Crystalline materials with broken inversion symmetry can exhibit a spontaneous electric polarization, which originates from a microscopic electric dipole moment. Long-range polar or anti-polar order of such permanent dipoles gives rise to ferroelectricity or antiferroelectricity, respectively. However, the recently discovered antiferroelectrics of fluorite structure (HfO 2 and ZrO 2 ) are different: A non-polar phase transforms into a polar phase by spontaneous inversion symmetry breaking upon the application of an electric field. Here, we show that this structural transition in antiferroelectric ZrO 2 gives rise to a negative capacitance, which is promising for overcoming the fundamental limits of energy efficiency in electronics. Our findings provide insight into the thermodynamically forbidden region of the antiferroelectric transition in ZrO 2 and extend the concept of negative capacitance beyond ferroelectricity. This shows that negative capacitance is a more general phenomenon than previously thought and can be expected in a much broader range of materials exhibiting structural phase transitions.