BiLSTM Predicts HPGe Detector Yield

Researchers led by Dongming Mei present a data-driven framework (v1 posted Feb 3, 2026) using a BiLSTM with multi-head attention trained on time-resolved growth data from 48 Czochralski crystal runs to predict final detector-grade fraction of high-purity germanium crystals. Using SHAP, the model finds impurity concentration and growth rate chiefly govern yield, offering interpretable guidance to improve production and scale HPGe detectors for rare-event experiments.