Work

Diffraction amplification enables imaging- and lens-free defect detection

Year: 2026

Type: article

Abstract: We demonstrate how the natural amplification of defect signatures via Fresnel diffraction can be harnessed for optical detection without imaging or lenses. In this work, we use the diffraction pattern itself as the detection domain, establishing a direct pathway from physical wavefront modulation to defect detection tasks that bypasses conventional image formation. In this paradigm, subtle defects are naturally amplified into distinctive diffraction fringes during propagation, a physical expansion that enhances their detectability without lenses. Computationally, we focus this scattered field using only a single inverse diffraction step, which relocalizes the expanded signal into a sharp saliency map at the defect site. This tight coupling of physical expansion and computational localization eliminates the need for imaging optics, phase retrieval or annotated data and enables a simple and low cost architecture with inherently high reliability. Because the detection operates directly on diffraction phenomena rather than reconstructed images, the method achieves fast processing and maintains strong sensitivity to phase type defects. Experimental and numerical results on semiconductor wafers and display panels at visible wavelengths under strong noise (σ 2 = 0.05) and extremely low contrast (C = 0.005) show that the method reliably detects defects at the scale of the working wavelength level. Under bandwidth and sampling constraints, the underlying diffraction model can be transferred naturally to the near infrared and even the extreme ultraviolet, which indicates strong potential for spectral scalability. These results position diffraction saliency as a scalable framework with simple hardware implementation for inline inspection in complex environments.

Source: Optics Express

DOI: 10.1364/oe.584244

Authors: Feiyu Duan, Shuxian Zhao, Yanqi Chen, Yunlong Qi, Yishi Shi

Institutions: Hebei University of Engineering, University of Chinese Academy of Sciences, Beihua University, Chinese Academy of Sciences, Aerospace Information Research Institute

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: National Natural Science Foundation of China, University of Chinese Academy of Sciences, Fundamental Research Funds for the Central Universities