ML Hit 99% Accuracy on Yield Prediction — The Factory Floor Ignored It

ML Hit 99% Accuracy on Yield Prediction — The Factory Floor Ignored It The pitch to bring ML into semiconductor FAB (fabrication facility) yield prediction has exploded over the past two years. Dig through ArXiv and you'll find N-BEATS+GNN for anomaly prediction, Transformer-based SPC precursor detection, semi-supervised defect segmentation, statistical difference scores for tool matching — no shortage of methods. Every paper reports high accuracy on test data. Some claim F1 > 0.9, AUC 0.99, classification accuracy in the 99% range. By the numbers, this looks like a solved problem. But the factory floor won't use them. Not because accuracy is insufficient. Because how accuracy is achieved doesn't match how production decisions are made. This article dissects 5 ArXiv papers to identify 3 structural reasons ML fails to penetrate semiconductor yield improvement — and the viable breach points despite those walls. The 5 Papers: What's Being Proposed 1. N-BEATS + GNN for Anomaly Predictio