A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing

The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. Such legacy approaches cannot fully capture the intricate, multi-modal shifts that either gradually erode product quality or trigger abrupt process disruptions. To surmount these challenges, we present M2D2 (Multi-Machine and Multi-Modal Drift Detection), an end-to-end framework that integrates data preprocessing, baseline modeling, short- and long-term drift detection, interpretability, and a drift-aware federated paradigm. By leveraging self-supervised or unsupervised learning, M2D2 constructs a resilient baseline of nominal behavior across numeric, textual, and categorical features, thereby facilitating the early detection of both rapid spikes and slow-onset deviations. An interpretability layer—using attention visualization or SHAP/LIME—delineates which sensors, logs, or batch identifiers precipitate each drift alert, accelerating root-cause analysis. An active learning loop dynamically refines threshold settings and model parameters in response to real-time feedback, reducing false positives while adapting to evolving production conditions. Crucially, M2D2’s drift-aware federated learning mechanism reweights local updates based on each site’s drift severity, preserving global model integrity at scale. The key scientific breakthrough of this work lies in combining advanced multi-modal processing, short- and long-term anomaly detection, transparent model explainability, and an adaptive federated infrastructure—all within a single, coherent framework. Evaluations of real WBG fabrication data confirm that M2D2 substantially improves drift detection accuracy, broadens anomaly coverage, and offers a transparent, scalable solution for next-generation semiconductor manufacturing.