Yield Prediction Model for Ingot Samples Based on Machine Learning and Data Augmentation

The preparation of high-performance cadmium zinc telluride (CZT) radiation detector materials requires efficient ingot-level quality assessment before full downstream wafer testing. This study proposes a machine learning framework that predicts the product-level yield of test wafers from IV and double-sided spectral measurements of a limited number of standardized evaluation wafers from the same ingot. To address the small number of ingots and wafer-level variability, ingot-level aggregate, A/B-side consistency, threshold-ratio, and distributional features were combined with intra-ingot bootstrap augmentation. Among the evaluated regression models, Random Forest achieved the best held-out test performance under a leakage-safe protocol, with an MSE of 0.021, an MAE of 0.125, and a Pearson correlation coefficient of 0.646; XGBoost showed comparable performance, with an MSE of 0.023, an MAE of 0.128, and a Pearson correlation coefficient of 0.601. In a top-22% screening experiment, the average true yield of ingots selected by Random Forest and XGBoost reached 63.71% and 60.40%, respectively, exceeding the empirical Rule_IV_Abs baseline of 59.08%. These results indicate that the proposed framework can provide useful ranking and prioritization support for early CZT ingot screening, while remaining a decision-support tool rather than a replacement for wafer-level inspection.