Unsupervised ML framework using PCA and K-Means predicts ferroelectric HZO capacitor performance on unseen dies with 5-10% MAPE for wafer-scale variability analysis.
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An Unsupervised Machine Learning-based Framework for Wafer Scale Variability Analysis and Performance Prediction of Ferroelectric Hf0.5Zr0.5O2 Thin Film Capacitors
Unsupervised ML framework using PCA and K-Means predicts ferroelectric HZO capacitor performance on unseen dies with 5-10% MAPE for wafer-scale variability analysis.