Why is motion position monitoring important in a Scrubber cleaning process?

In wafer cleaning processes, Scrubbers use Spray Nozzles to apply cleaning solutions onto wafer surfaces to remove particles and contaminants. If nozzle position, movement path, spray angle, or flow rate becomes unstable, it may result in uneven cleaning, contamination residue, wafer scratches, and yield loss. Therefore, every cleaning cycle should be monitored to ensure that the motion reaches the intended position.

Do Spray Nozzle angle and flow rate affect wafer quality?

Yes. The spray angle and flow rate directly affect the impact force and coverage area of the cleaning solution on the wafer surface. Incorrect angles or abnormal flow rates can cause insufficient cleaning, excessive impact force, or surface defects, affecting wafer quality and downstream process stability.

What motions are typically included in a Scrubber cleaning process?

In this case, the Scrubber cleaning sequence includes Z-axis lifting, X-axis movement, X-axis movement combined with the first cleaning cycle, and X-axis movement combined with the second cleaning cycle. These highly repetitive actions are ideal for machine learning model training and similarity-based trend analysis.

How does VMS-ML monitor Scrubber cleaning motions?

The VMS-ML Machine Learning Monitoring System captures dynamic signals from Scrubber operations through sensors and converts them into visualized features. The system learns normal repetitive cleaning behaviors and evaluates each subsequent cycle through similarity scoring to verify servo motor consistency and ensure that the Spray Nozzle reaches the designated position.

What are the benefits of implementing Scrubber motion monitoring?

Scrubber motion monitoring enables equipment condition management through edge computing, time-domain analysis, frequency-domain analysis, and visualized dashboards without storing large amounts of raw data. It helps determine maintenance requirements, verify post-repair performance, optimize alarm thresholds using AI trend analysis, and establish predictive maintenance management standards.

Case

Application

FAQ

Support

How to Ensure the Positioning Accuracy of the Scrubber Cleaning Process?

Q: What is the purpose of Dynamic Similarity in Scrubber monitoring?

Dynamic Similarity compares each cleaning cycle with a learned standard model. If Z-axis movements, X-axis motions, nozzle paths, or cleaning sequences differ significantly from the normal pattern, the system can identify abnormal conditions through similarity scores and trend analysis, helping engineers detect potential issues earlier.

Case｜How to Ensure the Positioning Accuracy of the Scrubber Cleaning Process?

In wafer cleaning, Scrubber machines commonly use solutions to remove particles from the wafer surface. Although the cleaning process appears simple, the effectiveness of the Scrubber directly impacts wafer quality. How can operational stability be ensured?

Wafer Cleaning

In wafer cleaning, Scrubber machines typically use solutions to remove particles from the wafer surface, effectively eliminating various surface contaminants while preventing surface defects or scratches. The spray nozzle's spraying angle and flow rate directly influence the impact force and pressure positioning, which are crucial factors affecting wafer quality.

Principle of Spray Nozzle
The spray nozzle is an atomizing device that utilizes internal nitrogen pressure to drive liquid at high speeds, creating atomized droplets through impact. These atomized droplets are sprayed onto the wafer surface, effectively removing microscopic particles. By ensuring that the spray nozzle reaches the designated position, it is also possible to track the movement trends of related components for better process management.

Monitoring Explanation

VMS-ML Machine Learning Intelligent Monitoring System
The system converts dynamic sensor signals into visual representations, allowing hidden process insights to be uncovered and used as a basis for future online monitoring. In addition, servo motors drive various components, and by utilizing the intelligent monitoring system, the consistency of each servo motor operation and the accuracy of the Spray Nozzle's positioning can be ensured.

Measurement Conditions

Scrubber Cleaning Process:
1. Z-axis lifting
2. X-axis horizontal movement
3. X-axis horizontal movement + first cleaning
4. X-axis horizontal movement + second cleaning

Single Wafer Cleaning Process

Simulated VMS-ML Machine Learning Monitoring System Measurement

Comparison of Four Wafer Cleaning Conditions

Dynamic Similarity

The system quickly learns and identifies repeated or partially repeated behaviors during the process, with VMS-ML scoring the movement similarity and conducting trend analysis management.

Measurement Conclusion

Benefits of VMS-ML Machine Learning

Equipment Integration: No need to integrate with equipment systems, start monitoring after sensor installation.
Time/Frequency Domain: Simultaneous management of time and frequency domain relationships with VMS-ML.

Edge Computing: No need for large storage space for raw data.
Visual Management: Graphical visualization of equipment operating status.
Focused Management: Visual analysis to pinpoint abnormal axial issues for maintenance.
Pre/Post Maintenance: Assess whether maintenance is needed and verify machine quality after servicing.
AI Trend Analysis: Fine-tuned thresholds and predictive maintenance standards.

VMS-ML Machine Learning Monitoring System

VMS-ML

Instantly intercept defective products.

FAQ

Why does the Scrubber cleaning process need to monitor action positions?
During the wafer cleaning process, the Scrubber sprays cleaning fluid onto the wafer surface through a spray nozzle to remove particles and contaminants. If the nozzle position, movement path, spray angle, or flow rate is unstable, it may cause uneven cleaning, residual contamination on the wafer surface, scratches, or a drop in yield. Therefore, it is necessary to monitor whether each cleaning action reaches the designated position.

Will the angle and flow rate of the spray nozzle affect wafer quality?
Yes. The spray angle and flow rate of the spray nozzle affect the impact force and pressure location of the cleaning fluid on the wafer surface. If the angle shifts or the flow rate is abnormal, it may lead to insufficient local cleaning, excessive fluid impact, or surface defects, thereby affecting wafer cleaning quality and subsequent process stability.

What actions does the Scrubber cleaning process usually include?
In this case, the Scrubber cleaning process includes Z-axis ascent, X-axis traverse, X-axis traverse plus the first cleaning, and X-axis traverse plus the second cleaning. These actions are repetitive, making them suitable for establishing a normal action model through machine learning for subsequent similarity judgment and trend management.

How does VMS-ML monitor Scrubber cleaning actions?
The VMS-ML machine learning intelligent monitoring system captures dynamic signals during Scrubber operation via sensors and converts them into visual images. The system learns the repetitive or partially repetitive behaviors in the normal cleaning process, then performs similarity judgment and scoring for each subsequent action. This helps confirm the consistency of servo motor actions and whether the spray nozzle moves to the designated position.

What is the purpose of Dynamic Similarity in Scrubber monitoring?
Dynamic Similarity is used to compare the similarity between each cleaning action and the normal model. If a Z-axis or X-axis action, nozzle movement path, or cleaning process differs significantly from the normal state, the system can indicate the anomaly risk through similarity scores and trend changes, helping engineers inspect the equipment status in advance.

What are the benefits of implementing Scrubber cleaning action monitoring?
After implementing Scrubber cleaning action monitoring, equipment operating conditions can be grasped through edge computing, time/frequency domain analysis, and visual management without the need to store large amounts of RAW DATA. The system helps confirm whether the equipment needs maintenance, checks the restoration quality after maintenance, and refines thresholds through AI trend analysis to establish predictive maintenance management specifications.