Memory-Efficient Artificial Intelligence Framework for Real-Time Multivariate Anomaly Detection

The semiconductor industry is rapidly expanding, driven by growing demands in artificial intelligence (AI), cloud computing, the internet of things, and the electrification of automotive and infrastructure. As manufacturing processes grow more complex and dynamic, accurate real-time anomaly detection becomes critical for maintaining yield and reliability. However, conventional statistical methods lack adaptability in dynamic process conditions, while deep learning models are typically too resource-intensive for edge deployment. Herein, we present a lightweight yet robust AI-based anomaly detection framework for real-time, sensor-specific semiconductor manufacturing. Our framework incorporates a four-layer convolutional neural network trained on unsupervised dual-segment inputs with hierarchical layer-wise objectives. Using only 3,783 parameters for 49 input features, the framework achiev…