How to Ensure the Stability of Mask Aligner Exposure Machines?

In semiconductor manufacturing, photolithography is used to expose circuits onto wafers using light waves. Any abnormal vibration during the process can easily lead to defective products. How can detection and prevention be effectively implemented?

Mask Aligner Exposure Machines

Mask aligners are key devices used in the production of MEMS, optoelectronics, and large-scale integrated circuits. They can be classified into two types: contact aligners, where the mask directly contacts the wafer during exposure, and projection exposure systems such as steppers and scanners, which use short-wavelength lasers to project images onto the wafer, achieving exposure patterns smaller than the mask itself.

Operating Principle of Mask Aligners
The basic steps for producing integrated circuits include using a mask to remove the protective layer on the wafer surface. The wafer is then immersed in an etchant, which corrodes the exposed areas, forming the circuit. The mask aligner uses ultraviolet excimer lasers to remove the wafer’s protective film through the mask.

A single wafer can produce dozens of integrated circuits. Based on mask aligner design, they can be classified into two types: fixed masks, where the mask and wafer are the same size and remain stationary, and moving masks, where the mask and circuit are of equal size and move with the exposure machine. In the latter case, the mask remains in a fixed position relative to the center of the exposure machine, ensuring higher precision by consistently using the center of the focusing lens. Source: Wikipedia

Solutions and Monitoring Explanation

VMS-ML Machine Learning Intelligent Monitoring System
Gude Technology performs multi-axis measurements on the exposure machine's vibration isolation platform to analyze axial changes. The measured vibration data is visualized, converting numerical values into intuitive graphical representations. Combined with operator experience, these visual insights help identify hidden process issues, establish maintenance thresholds, and provide a basis for online monitoring.

The VMS-ML machine learning intelligent monitoring system learns standard operational behavior as a reference for monitoring and diagnosing each motion of the machine. By identifying anomalies or instabilities in specific actions, predictive maintenance can be performed early to prevent unexpected failures.