An image analysis-based Reflection Coefficient (RC) estimation method of femtosecond laser surface processing for the blackening of X-ray imaging sensor shell is proposed. The Support Vector Regression (SVR) is used for RC computation and both an offline and an online steps are considered in this method. Regarding offline step, the typical laser process parameters are set to perform surface processing and Scanning Electron Microscope (SEM) images are recorded. Then a series of image features are computed and both the computed image features and typical laser parameters are used to train SVR: the training dataset includes the laser line space, laser beam diameter, natural logarithm of laser power divided by laser frequency, and image features of Gray-Level Co-occurrence Matrix (GLCM); the supervising data are laser ablation diameters. As for online step, when SEM image data are recorded after laser processing, the trained SVR is used to predict laser ablation diameter and then the RC can be computed by laser ablation model. Many experiment results have verified the effectiveness of our proposed method, and the RC estimation accuracy can be better than 90.0%.
The influence of pixel design on charge transfer, mainly on potential barrier and potential pocket, is investigated in this paper. By simplifying the interface region between the pinned photodiode (PPD) and the transfer gate (TG), an intuitive and simple method for analyzing the potential barrier and pocket is presented. Through TCAD simulations, the validity of the analysis method is verified, and the influence of some key dimension and process parameters in PPD pixel design on the potential barrier and pocket is clarified. The whole study help improve the understanding of the charge transfer limitation mechanism, and can achieve high charge transfer efficiency and low image lag noise in the PPD pixel design of CMOS image sensors (CISs).
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