Multi-modal sensor fusion and edge-AI for Early Detection of Micro-Contamination Events in ISO 5 Cleanrooms During Diagnostic Test Kit Manufacturing

Maintaining ultra-clean conditions in ISO 5 environments is fundamental to ensuring the sterility, accuracy, and regulatory compliance of diagnostic test kit manufacturing. Even microscopic contamination events airborne particulates, microbial aerosols, chemical residues, or electrostatic disturbances can jeopardize reagent integrity, compromise microfluidic components, and trigger large-scale batch failures. From a broader perspective, the increasing complexity and throughput of modern diagnostic production lines demand continuous, automated, and highly sensitive monitoring frameworks capable of identifying early deviations long before they manifest as product defects. Traditional cleanroom surveillance methods, such as periodic particle counting or manual microbial sampling, remain limited by latency, human dependency, and insufficient spatial-temporal resolution. Narrowing the focus, this study proposes an integrated multi-modal sensor fusion and edge-AI framework designed specifically for early detection of micro-contamination events in ISO 5 cleanroom environments. The system aggregates complementary sensor streams including airborne particle counters, optical scattering sensors, volatile organic compound (VOC) monitors, environmental microbial fluorescence detectors, differential-pressure sensors, electrostatic field monitors, and surface-deposition imaging modules to create a real-time, multi-dimensional profile of cleanroom dynamics. These data streams are processed locally using edge-AI modules optimized for ultra-low-latency inference, enabling rapid detection of spatiotemporal anomalies that may signal emerging contamination risks. A hybrid analytics architecture combining anomaly detection models, spiking neural networks, and temporal ensemble classifiers provides both sensitivity to subtle shifts and robustness against false-positive alerts. The framework supports predictive interventions by identifying contamination precursors, such as airflow instability, equipment-generated micro-debris, or personnel-induced particulate disturbances. By enabling continuous monitoring and early warning, the proposed system strengthens quality assurance, reduces wastage, minimizes production interruptions, and enhances regulatory confidence in high-throughput diagnostic kit manufacturing.