id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
american_scientific_journal-11608	Vijayaprabhuvel Rajavel	Novel Machine Learning Approach for Defect Detection in DFT Processes	2025	10	.pdf	application/pdf	4241	246	40	Stage Input ML Task Output Data consolidation Raw fail logs, netlists, optional BIST – Unified dataset of test logs + design features Feature engineering SCOAP metrics, adjacency, logic depth – Cleaned feature vectors (per die/test pattern) By leveraging classification algorithms such as support vector machines and neural networks, ML-driven approaches can reduce test pattern generation time, improve bridging-fault coverage, and streamline board- or wafer-level screening.	cache/american_scientific_journal-11608.pdf	txt/american_scientific_journal-11608.txt