# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 7 Future Work  
Although M2D2 lays a solid foundation for robust multi-machine, multi-modal drift detection, several critical research directions remain underexplored within the current framework. One significant avenue concerns deeper interpretability and visualization, where the employment of advanced graph-based techniques--such as graph neural networks (GNNs) or knowledge graphs--could reveal latent relationships among sensor signals, textual logs, and high-dimensional data. While M2D2 effectively integrates diverse sensing modalities for anomaly detection, it has yet to thoroughly investigate how these more sophisticated tools could illuminate root causes or interactive effects in a more detailed and intuitive manner. Another underexamined aspect relates to the adaptability of federated learning within heterogeneous industrial settings. M2D2 demonstrates the feasibility of drift-aware federated learning, but it does not fully delve into dynamic strategies for handling highly variable data distributions across different machines or production lines. Devising new methods--such as hierarchical grouping based on shared process characteristics or variable polling frequencies informed by anomaly rates--could offer more granular control over federated model updates, thereby improving both local customization and global consistency. Moreover, M2D2 has not yet addressed the integration of DT technology for real-time simulation and parameter tuning. While the framework can detect parameter drifts with high sensitivity, further research is needed to systematically evaluate how a DT environment might model the operational impact of these drifts. By iteration in a virtual space, M2D2 could refine or validate parameter adjustments prior to implementing them in the physical production line, thus reducing risk and downtime. Additionally, although M2D2 benefits from active learning, it has not fully explored more advanced schemas that incorporate semi-supervised and self-supervised techniques. A more rigorous approach to identifying and prioritizing valuable unlabeled instances for labeling or verification could substantially accelerate the discovery of novel drift patterns. Finally, the potential for PdM has been recognized but not thoroughly integrated into the M2D2 pipeline. By combining real-time anomaly alerts with historical maintenance records and sensor-based health assessments, future extensions could deliver more proactive strategies, estimating Remaining Useful Life (RUL) and scheduling interventions before failures occur. These unexplored avenues collectively point to a rich landscape for future investigation that could further enhance M2D2's applicability, robustness, and overall impact on smart manufacturing. **Author Contributions:** C.-Y.L. was responsible for conceptualization, methodology, software development, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, and visualization; [PERSON]-L.T. oversaw supervision and funding acquisition; T.-H.T. handled project administration and validation. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was funded in part by the U.S. National Science Foundation under grants ERC-ASPIRE-1941524; DUE-2216396 and U.S. Department of Education under grants Award #P116S210004; Award #P120A220044. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to Confidentiality at Semiconductor Companies. **Acknowledgments:** The authors sincerely thank Infineon Technologies Austria AG, Villach, for their support of the Wide Bandgap Semiconductor (WBG) Project, including access to real-world data and technical expertise.