Method and model for three-dimensional characterization of molybdenum disulfide sample based on machine learning, and use thereof

A method for a three-dimensional characterization of a molybdenum disulfide sample based on a machine learning, comprising the following steps:

• (1) an optical image acquisition:
• preparing the molybdenum disulfide sample, and acquiring an optical image of the molybdenum disulfide sample through a microscope;
• (2) an image processing:
• subjecting the optical image acquired in the step (1) to a denoising and a mean filtering;
• (3) an atomic force microscopy (AFM) characterization:
• performing the AFM characterization in a same local region for the optical image acquisition to acquire an AFM height data of a first local region of the molybdenum disulfide sample;
• (4) a region of interest (ROI) segmentation:
• segmenting a second local region, corresponding to an ROI indicated by an AFM characterization result acquired in the step (3), in the optical image processed in the step (2);
• (5) an image feature extraction and a dataset establishment:
• extracting a color feature dataset of the second local region segmented in the optical image;
• using the AFM height data as a target dataset;
• and combining each pixel datum in the color feature dataset of the optical image with a respective pixel datum in the target dataset of the AFM height data to form a feature dataset of a height image of the molybdenum disulfide sample;
• (6) a dataset splitting and a machine learning model training:
• splitting the feature dataset into a training set and a testing set, wherein the training set is used for training a model, and the testing set is used for validating an accuracy of the model;
• constructing the model by a random forest algorithm based on the training set, and training the model by controlling a number of random trees based on the testing set to improve the accuracy of the model;
• and finally exporting the model;
• (7) a new image import operation:
• processing a target molybdenum disulfide sample according to the step (1) and the step (2) to obtain the optical image, extracting a color feature value of the optical image, bringing the color feature value into the model acquired in the step (6), and calculating a height data of the target molybdenum disulfide sample;
• and
• (8) a three-dimensional image filtering:
• filtering a three-dimensional image acquired in the step (7) to remove a local noise and a local abnormal point to acquire a final three-dimensional characterization image.