¶ UC17 - Intelligent Contamination Management and Prescriptive Control Plans and Process Flows
This use case develops adaptive and automated contamination management for semiconductor fabs. It combines innovative cleanroom sensors, mobile sensor systems, FOUP cleaning optimization, edge computing, predictive maintenance, control systems, and big-data analytics to detect and manage contamination risks more efficiently. The goal is to create adaptive control plans and process flows that reduce contamination-driven process issues and unnecessary offline analyses. The expected outcome is higher yield, better productivity, faster development cycles, optimized use of DI water and energy, and reduced corrective maintenance time.
| # |
Technology Name |
Description |
| 1 |
Sensors and actuators for cleanroom monitoring |
Sensor systems are developed for fully automated cleanroom control in semiconductor factories. Static sensors using a meander structure are developed to detect electrical signals upon exposure to hazardous gases. These Si-based AMC sensors support stable manufacturing yields by enabling earlier and more systematic contamination detection. |
| KPI # |
Related Technology |
KPI Target |
| 1 |
Cleanroom Sensors |
First results on sensors with meander structures show good feasibility. Using the sensors, reaction time will be reduced from 8 hours to less than 1 hour. |
| 2 |
Cleanroom Sensors |
First tests on Si sensors show good results. By the end of the project, it will be demonstrated that manufacturing on 300 mm wafers is possible. |
| 3 |
Cleanroom Sensors |
Validate MQTT protocols for data sharing and contamination control algorithms. Reduce reaction time from 8 hours to less than 1 hour. |
| 4 |
Cleanroom Sensors |
Deploy pilot visualization tools and heat maps for contamination data analysis. Visualization dashboards should be available. |
| KPI # |
Baseline |
| 1 |
Current systems, such as APA302 equipment by Pfeiffer Vacuum, use highly sensitive sensor technology such as Picarro sensors that can detect corrosive gases even at extremely low concentrations. However, sensor networks and sensor integration are not possible. |
| 2 |
No separate baseline value provided. |
| 3 |
No separate baseline value provided. |
| 4 |
No separate baseline value provided. |
| KPI # |
Validation Method |
| 1 |
Develop a demonstrator for static sensors using a meander structure as the basis for a prospective sensor network. |
| 2 |
Design sensors to detect electrical signals upon exposure to hazardous gases and construct sensors on silicon wafers. |
| 3 |
Use ML for prediction and root-cause analysis, and validate MQTT-based communication for data sharing and contamination control. |
| 4 |
Develop corrosion monitoring using meander probes and a plastic chamber with integrated sensors to measure contamination levels of gases such as HCl. Develop Si-based AMC sensors in the cleanroom, including sensor coating, coating-thickness determination, and demonstrator testing based on conductivity changes. |
| 5 |
Prepare the system by spin-coating with optimized parameters on different electrode geometries and materials. |
- Industrial process phases made more effective and precise through integrated and tailored AI algorithms, methods, and tools.
- 20% improvement in prediction precision.
- Improved overall productivity of the manufacturing factory by approximately 20%.
- Self-adjusted/adapted manufacturing processes above 15%.
- Providing documented methods for dynamic application of tools to improve sustainability.
- Improved performance and faster time-to-market.
The use case contributes to more effective and precise semiconductor manufacturing by developing cleanroom sensor systems, sensor-network concepts, contamination control algorithms, MQTT-based data sharing, ML-supported prediction and root-cause analysis, and visualization dashboards for contamination monitoring. The reduction of reaction time from 8 hours to less than 1 hour directly supports faster response to contamination events and can reduce yield losses, corrective maintenance effort, and unnecessary offline analyses. The development of Si-based AMC sensors and sensor demonstrators supports automated and scalable contamination monitoring in cleanroom environments. Visualization tools, heat maps, data lake applications, and KNIME workflows further support better decision-making, improved productivity, and more adaptive contamination management processes.
| AI Toolbox Tool |
Use within the Use Case |
| Time-Series Anomaly Detection Tool |
Used to detect abnormal temporal patterns in cleanroom sensor streams and contamination-monitoring data. It supported earlier identification of contamination events and helped reduce reaction time from 8 hours to less than 1 hour. |
| Tabular Prediction Tool |
Used to train ML models on structured cleanroom, sensor, contamination, and process data. It supported prediction and root-cause analysis for contamination events and helped improve contamination-control decisions. |
| Explainability Tool |
Used to interpret ML-based contamination prediction and root-cause analysis results. It helped identify which sensor readings, process variables, or environmental conditions contributed to contamination risks. |
| Device-Level Quality Detection Tool |
Used to connect contamination-related sensor features with device-level quality risks and possible root causes. It supported the analysis of how airborne molecular contamination may affect semiconductor manufacturing yield and process stability. |
| Lag-Llama |
Used to forecast contamination-related trends from cleanroom sensor time-series data. It supported proactive monitoring and earlier preparation of control actions before contamination levels became critical. |