Objective Femtosecond laser micromachining technology has excellent three-dimensional (3D) processing capabilities and provides significant advantages in the production of experimental materials with complex 3D structural features. However, the continuous improvement of ablation efficiency and accuracy is still an eternal topic. The femtosecond laser ablation of monocrystalline silicon is accompanied by the luminescence phenomenon derived from the plasma. During the movement of the three-degree-of-freedom motion control platform in 3D space, the plasma spot produces different forms, particularly during the reciprocating ablation process in the X direction, and two distinct spot forms appear. The trailing direction of the light spot is the upper and lower left when moving forward and backward, respectively. The optimized cascaded support vector machine (SVM) classifier is used to accurately classify and analyze the light spot and can explore the ablation efficiency and accuracy in different ablation directions.
Methods First, the SVM classifier uses the feature of the spot centroid to classify the light spots at the first level. Then, we introduce the means of upper and lower peer lines and obtain two types of light spots. One type is correctly classified into the corresponding ablation direction, called the R light spot, which includes the first-level UP light spot (the trailing direction is the upper left) and first-level DN spot (the trailing direction is the lower left). The other type is incorrectly classified into the opposite ablation direction, called the E spot. Next, the first-level DN spot is superimposed, and the average value is calculated to obtain the average spot. To further obtain the standard model to maximize the similarity of each first-level DN spot, the mean spot is placed into a generative adversarial network (GAN) for training and generation. Compared with random noise, the use of average light spots can reduce the number of training and produce a final generated image more similar to the standard model. Finally, SSIM is used to calculate the similarity between the E spot and standard model, and the E spot is classified using the second-level SVM to generate the second-level UP and DN. Combining the E spot with the first-level UP and DN spots, the final classification result is achieved.
Results and Discussions Using this method, the classification accuracy is 100% under the processing power of 10 mW. In the entire ablation cycle, 34 spots are produced corresponding to the two trailing directions in the two ablation directions. Under 20 mW, the classification accuracy is also 100%. Each half of the ablation cycle produces 33 light spots in the same trailing direction. The deviation is the classification result under 50 mW, and its accuracy is 98.5%. There should be 66 light spots in the same motion state every half cycle; in the second half cycle, one light spot is not correctly classified in the classification result. In the entire time series, only two spots are misclassified, which is close to 100%, and the classification effect is significantly improved. To verify the accuracy of the cascaded SVM classifier in the classification of different states of light spots generated under different ablation directions, three classification methods of histogram of oriented gradient (HOG)-SVM, local binary mode (LBP)-SVM, and Gaussian pyramid (GP)-SVM are compared. Among them, HOG is constructed by calculating and counting the histogram of the gradient direction of the local area of the image operating on the image local grid unit and maintaining good invariance of the image deformation. LBP is an operator that can effectively measure and extract local texture information of an image. It has significant advantages such as nonrotational deformation and gray invariance. GP downsamples the image to obtain partial information of the image. Compared with the traditional HOG-SVM, LBP-SVM, and GP-SVM classification methods, the classification accuracy of the cascaded SVM classifier is increased by 5 to 9 percentage points, 12 to 16 percentage points and 9.0 to 15.5 percentage points 10, 20, and 50 mW, respectively. The cascaded SVM classifier delivers nearly 100% classification accuracy for the spot when using each level of processing power, which has obvious advantages.
Conclusions To classify the different forms of light spots in the femtosecond laser ablation process of single crystal silicon, an optimized cascaded SVM classifier is used. First, the first-level classification is performed based on the spot centroid feature. Then, the standard model is established by generating confrontation GAN. Next, the second-level SVM classification is performed using the structural similarity SSIM of the misclassified spot and the standard model. The classification results are remarkable. A better understanding of the movement state of the light spots can aid further exploration of the law of ablation. It has an indelible effect on the improvement of ablation efficiency and accuracy.
