Application of principal component analysis and clustering methods in the discrimination of parameters in HgCdTe crystals
A method for selecting parameters in HgCdTe crystals has been proposed,utilizing Principal Component Analysis(PCA)and clustering methods,with the establishment of a data model for screening the parameters of HgCdTe crystals.Within the model,the initial crystal data undergoes a cleaning and analysis process.PCA is employed for dimensionality reduction,and the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algo-rithm is used to identify the densest regions within the crystal data.Furthermore,the high-performance chip data,ob-tained after post-processing,is utilized to fit boundary ellipses for high-quality HgCdTe crystal parameters.These ellips-es act as criteria for identifying high-quality crystals.The model is capable of generating crystal ratings based on input electrical and optical parameters with a coverage rate exceeding 90%.
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