# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 1 Introduction
Most existing solutions specialize in numeric time series but struggle to meaningfully incorporate textual or categorical signals, leading to potential blind spots in the detection mechanism [11]. Second, drift behaviors are not confined to single manufacturing steps. Subtle shifts in an upstream epitaxy (Epi) process, for example, may only manifest downstream in device electrical measurements. Methods focusing on single-machine anomalies may therefore fail to account for cross-machine or cross-step dependencies [12]. Third, the difference between short-term abrupt anomalies (e.g., a sudden spike in gas flow) and long-term gradual drifts (e.g., progressive chamber contamination) cannot be captured by a one-size-fits-all threshold or model. Balancing immediate detection with longer-horizon insights requires a multi-timescale approach [13]. Fourth, the necessity for human oversight remains crucial in high-stakes production environments. Although explainable AI (XAI) techniques like attention maps, SHAP, or LIME have been increasingly applied, many anomaly detection pipelines omit or undervalue the role of interactive feedback loops through which human experts can refine model thresholds and confirm genuine anomalies [14]. Finally, with semiconductor manufacturing often distributed across multiple fabrication facilities and machine clusters, the use of FL and distributed sensor networks is becoming more prevalent. Classical FL methods, however, are ill-equipped to handle heterogeneous or site-specific drifts, risking the dilution of the global model by drifting clients [15]. These gaps highlight the need for an integrated framework capable of unifying multi-modal data preprocessing, robust baseline modeling, interpretable short-term and long-term drift detection, active learning for continuous adaptation, and drift-aware federated coordination. To meet this challenge, we propose the M2D2 framework. M2D2 combines several design objectives that collectively address the shortfalls of existing methods: (1) a systematic approach to combining numeric, textual, and categorical inputs, ensuring that crucial signals are not overlooked; (2) a dual-level drift detection pipeline that flags both acute anomalies in real time and gradual shifts over extended windows; (3) interpretable modules that pinpoint root causes of drift, enhancing trust and transparency for engineers and decision-makers; (4) an active learning loop that refines detection thresholds and model parameters based on timely human or automated feedback; and (5) a drift-aware federated learning strategy that synthesizes local models without being jeopardized by severe parameter mismatches in individual machines. Although this paper does not demonstrate the direct integration of drift detection outcomes into predictive maintenance (PdM) or digital twin (DT) systems, it is nonetheless important to emphasize that robust detection of parameter drift is pivotal for these advanced manufacturing paradigms. PdM strategies rely on the early detection of equipment wear or process degradation to schedule cost-effective interventions, and DTs depend on timely, accurate process data to maintain high-fidelity virtual replicas of real-world production environments. Failing to capture subtle drifts at the source can undermine both the quality of PdM insights and the reliability of DT simulations, ultimately impacting yield, operational costs, and strategic decision-making in WBG manufacturing. By consolidating state-of-the-art anomaly detection mechanisms into a holistic architecture, we envision M2D2 not only as a technical innovation but also as a critical enabler of stable, efficient, and reliable WBG semiconductor production. The framework's agility in handling multi-modal data, its adaptability to short- and long-term changes, and its capacity to operate robustly across multiple facilities all align with the evolving demands of next-generation manufacturing.