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A Study on Condition-Based Maintenance for Wafer Table Edge Degradation in Photolithography Equipment
Kyunghwan Joo1,2, Kwang Hoon Lee2,3, Jae Wook Jeon1
1Department of Semiconductor and Display Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces a new method using Geometry-based Optical Focus Metrology (GOFM) to detect wafer table wear early. This enables proactive maintenance, preventing defects and improving semiconductor manufacturing yield and equipment uptime.
Area of Science:
- Semiconductor Manufacturing
- Optical Metrology
- Predictive Maintenance
Background:
- Wafer table wear in Deep Ultraviolet (DUV) immersion photolithography causes Edge-Roll-Off (ERO).
- Conventional metrology struggles to distinguish ERO from process noise.
- Early detection of ERO is crucial for preventing Critical Dimension (CD) defects.
Purpose of the Study:
- To develop a condition-based maintenance method for early detection of wafer table edge deterioration.
- To utilize Geometry-based Optical Focus Metrology (GOFM) for isolating focus residuals.
- To establish a data-driven approach for proactive maintenance in semiconductor manufacturing.
Main Methods:
- Implemented GOFM to isolate focus residuals in the 140-147 mm wafer table radius.
- Developed a dual-indicator system: Range Percentile 97% and Slope × 3.
- Analyzed long-term time-series data from High-Volume Manufacturing (HVM) scanners.
Main Results:
- Confirmed a strong correlation (R2=0.93) between the dual indicators and hardware degradation.
- Demonstrated that Slope × 3 drift trajectory predicts mechanical failure before defects occur.
- Designed an automated condition-based maintenance architecture with an OR-logic decision gate.
Conclusions:
- The proposed GOFM-based method enables early detection of wafer table edge deterioration.
- The dual-indicator system accurately quantifies hardware degradation and predicts failure.
- An automated maintenance system transitions from time-based to data-driven scheduling, enhancing yield and uptime.
