Advances in subsurface defect detection techniques for fused silica optical components: A literature review

Abstract

Fused silica is widely used in high-power laser systems, astronomy and military fields due to its excellent optical and physical properties. However, Subsurface defects(SSDs), such as microcracks, scratches, plastic deformation, and pits are often formed during mechanical processing, which can seriously reduce the optical performance and durability of fused silica. This paper first discusses the formation mechanism of SSDs in fused silica due to mechanical stress, thermal effects, etc. during mechanical processing such as cutting, grinding, and polishing. Then, the commonly used destructive and non-destructive detection methods are reviewed, and the principles and latest progress of each technique are discussed. The potential of deep learning algorithms for SSD detection is also examined. Finally, future directions for research and development are proposed to provide a valuable reference for researchers in the field.

Keywords

• Fused silica
• Subsurface damage
• Defect detection
• Deep learning