Design of detection system for subsurface particle defects in quartz glass

Optical components are essential for semiconductors, instrumentation systems, and other research directions. At present, various industries have increasingly high requirements for the quality of optical components, and quartz glass is its main processing material, so it is necessary to detect defects. However, surface defect detection cannot meet the needs of detection, and subsurface defects have become a key technology. As the scale of the subsurface defects decreases, the detection difficulty increases dramatically, and the main reason is that the interference caused by the surface defects increases gradually. In this paper, an ellipsoidal mirror is used to collect the scattered light from the defects, and by analyzing and simulating the scattered field distribution of the surface particle defects and the subsurface particle defects of quartz glass, it is found that the signal of the subsurface particle defects is stronger than the signal of the surface particles in a certain region (region B). Based on this finding, we propose an aperture fabrication method to allow only the scattered light from the B region to be received by the photomultiplier tube, while the light from the other regions is blocked, substantially enhancing the signal-to-noise ratio of the subsurface defect signal. The experimental sample is a piece of quartz glass with a small amount of dust particles present on both surfaces. We cleaned the surface, sprinkled iron powder particles on it, and then inverted the sample so that the iron powder particles adhered to the bottom surface of the sample to realize the detection of the subsurface defects. The detection result of removing the aperture shows that the signal has strong randomness, and the iron powder particles cannot be detected. The detection results after adding the aperture show that iron powder particles can be detected. The experimental comparison proves the correctness of the theory and the importance of aperture in subsurface defect detection.