Detection Canability Improvement Technology For Weak Defects On Smooth Surfaces Based On Dark Field Scattering

Optical components with precision surfaces play an important role in various high-tech instruments and equipment systems,and the detection of surface defects is a significant part of quality control of the precision optical components.In optical systems,scattering induced by surface defects will reduce the utilization efficiency of energy or even lead to severe damage to those with high-energy lasers.With the wide application of the optical components,the automatic and quantitative detection of surface defects becomes increasingly urgent.In contrast to the traditional manual visual inspection and other detection techniques,the surface defect detection system established exploiting the dark-field scattering technique can not only provide high sensitivity,but also take into account both the efficiency and accuracy,thus gradually becoming one of the best approaches to realize the automatic detection of precision surface defects.However,with the continuous improvement of the precision of advanced optical systems such as the inertial confinement fusion and the extreme ultraviolet lithography,and also the continuous development of optical manufacture technologies such as magneto-rheological finishing and single point diamond turning,the detection requirements for surface defects have become higher and higher.On the one hand,sufficient detection sensitivity for small-size defects is required to ensure the effectiveness and safety of optical components for use.On the other hand,to obtain a comprehensive evaluation report containing hazard assessment and cause analysis of defects,the ability of quantitative detection,including defect size metrology and defect classification,has become increasingly significant.It is difficult for the existing detection systems based on dark-field scattering to meet the increasing detection requirements,so it is of great significance to study the improvement technology of detection ability.To this end,this paper focuses on the improvement of the detection ability of the surface defect detection system based on dark-field scattering from three aspects:(1)how to improve the scattering signal quality of defects;(2)how to build accurate defect images,and(3)how to improve the discrimination ability of the defects with different types.It mainly includes the dark-field scattering theoretical model and simulation analysis of surface defects,the improved system scheme based on the spot scanning and multichannel and the corresponding calibration technique,and the surface defect classification method based on polarization characteristics.The specific research contents of this paper are as follows:First,the basic working principle and advantages of the dark-field scattering technique for defect detection are introduced in combination with the existing surface defects evaluation system(SDES),and then the limitations of this typical system under current detection requirements are pointed out.To explore the approach of improving the detection ability,a surface defect dark-field scattering theoretical model is established to simulate and analyze the scattering distribution characteristics of defects with different types,sizes and directions.And a scattering intensity model in a limited aperture is established to simulate the actual scattering intensity collected by the optical system in the dark-field scattering condition.According to the results of theoretical model and simulation analysis,the main means to improve the detection ability of the system are summarized,which will provide reliable theoretical guidance for the design and improvement of defect detection system based on dark-field scatteringOn this basis,a spot scan multichannel surface defect evaluation system(SSM-SDES)is proposed.Details about the optical system design and scanning strategy are discussed.To enhance the image quality,an image reconstruction method capable of suppressing signal noise through a weighted average strategy is studied.In the SSM-SDES,the design of a large aperture scattered light collecting path improves the detection sensitivity for weak defects,and the multichannel design lays a hardware foundation for the accurate classification of surface defects However,the existing system deviations will cause distortions and even a missing area in the defect image which is reconstructed from the acquired raw data based on the scanning trace.To address this issue,a system calibration method is proposed with the parameterization of these deviations and the modeling of practical scanning trace.A constraint function,to characterize the straightness and scale errors in the image,is defined.Then an optimization is implemented to minimize it and hence to obtain the optimal estimate of the system deviations.Using the proposed calibration and reconstruction method,a complete defect image with low noise and low distortion can be obtained;this will greatly improve the detection accuracy and reliability of the system.To solve the problem that the two types of defects,dig and dust particle,are difficult to distinguish in the traditional dark-field scattering detection system,a polarization characteristics-based classification method of digs and dust particles(PCCDD)is proposed.After the analysis of the polarization characteristics difference of digs and dust particles and the feasibility of classification with this difference,a general structure combining the dark-field scattering and polarization measurement is established,and the extraction method of a polarization feature that relates only to the polarization characteristics of defects is determined through theoretical derivation.An optimization algorithm is then implemented to maximize the separability on the polarization feature between digs and dust particles.The algorithm exploits a maximum probability density separable method to quantitatively evaluate the separability and a genetic algorithm to solve the nonlinear minimization problem of the evaluation function.With that,the optimal polarization measurement state can be obtained and a classifier is built.Finally,the specific implementation of this classification method in different dark-field scattering surface defect detection systems is discussed.The PCCDD makes full use of the difference of polarization characteristics between digs and dust particles,and the extracted features have high separability,so it can effectively improve the defect classification ability of the systemExperiments are carried out to verify the proposed system and methods.An experimental platform of the SSM-SDES is built and a series of standard defect plates with different depths are fabricated.System calibration and image reconstruction are carried out,and the calibration precision and image quality are assessed.The maximum straightness error in the reconstructed defect image is within 1.8 pixels and the scale error is within 0.7 pixels,indicating that the system deviations can be effectively corrected by the calibration method.Then,experiments are carried out on the detection of small-size surface defects with different depths,the imaging results are compared to those acquired by the SDES.The results show that the SSM-SDES has better performances in detection sensitivity,imaging uniformity,and signal-to-noise ratio.For weak defects with a depth of tens of nanometers,the minimum detectable dig diameter of the system is better than 1.6?m and the minimum detectable scratch width is better 0.5?m.Finally,a dark-field scattering polarization measurement verification system is established.And the normalized Mueller matrices' datasets of digs and dust particles are built through experimental measurement.On this basis,the effectiveness of the PCCDD is verified.The classification accuracy for real digs and dust particles reaches 90.5%,which has been greatly improved compared to the original classification method based on traditional dark-field images.At last,the works mentioned above are summarized and the future research direction is pointed out.