Anomaly detection and prediction in VLSI design workflows

This study addresses the problem of intelligent anomaly prediction during the early stages of very-large-scale integration (VLSI) design, under the constraints of increasingly complex topologies and modern technological standards. The problem formulation includes the formalization of criteria for anomaly detection, such as congestion zones, topological conflicts, and routing violations. To enhance classification accuracy and adaptability to diverse design data structures, a hybrid architecture is proposed that combines metaheuristic methods with machine learning algorithms. А computational experiment was conducted using both synthetic and publicly available datasets, demonstrating the effectiveness of the stacking model and graph neural networks in achieving high prediction quality within acceptable training times. The study also explores the impact of cross-validation and class balancing on resistance to overfitting. The obtained results confirm the practical applicability of the proposed approach in modern computer-aided design (CAD) systems. This article will be of interest to specialists in design automation and intelligent analysis of VLSI systems, as well as researchers working on hybrid methods in engineering applications.