Patent Application
20240203019
The present disclosure belongs to the technical field of two-dimensional (2D) material detection, and relates to a method and model for three-dimensional (3D) characterization of a molybdenum disulfide sample based on machine learning (ML), and a use thereof.
With the continuous emergence of two-dimensional materials, the excellent mechanical, electrical and optical properties of 2D materials have attracted widespread attention. Among the 2D materials, molybdenum disulfide is an important raw material for lubricants and has diamagnetic properties. Molybdenum disulfide has an energy bandgap of 1.8 eV, and a single-layer molybdenum disulfide transistor has an electron mobility up to about 500 cm2/(V·s) and an on-off current ratio up to 1×108. Therefore, molybdenum disulfide can be widely used in the field of nanotransistors. Single-layer molybdenum disulfide grown by chemical vapor deposition (CVD) is prone to introduce an impurity, thereby affecting device performance. In contrast, naturally occurring molybdenum disulfide in its bulk form has a uniform texture, and single-layer molybdenum disulfide prepared by a micromechanical exfoliation method has more excellent properties than that prepared by the CVD method. Therefore, the micromechanical exfoliation method can provide researchers with test samples with more excellent properties. However, since light is reflected at the boundary layers between molybdenum disulfide and silicon dioxide and between silicon dioxide and silicon and interferes with each other, molybdenum disulfide with different thicknesses shows different colors in the optical image. In recent years, with the maturity of the machine learning (ML) technology, great breakthroughs have been made in the k-nearest neighbors (KNN) algorithm, the random forest (RF) algorithm and other ML algorithms. However, although ML has been gradually applied in various industries, its use in the field of 2D materials is still insufficient due to the lack of suitable feature extraction methods.
The present disclosure provides a method and a model for three-dimensional (3D) characterization of a molybdenum disulfide sample based on machine learning (ML), and a use thereof. The present disclosure performs the 3D characterization of the molybdenum disulfide sample through optical imaging, and achieves the higher characterization accuracy.
The present disclosure achieves the above technical objective through the following technical means.
A method for 3D characterization of a molybdenum disulfide sample based on ML includes the following steps:
Further, the optical image is acquired by the microscope under a linearly adjustable light source, and one optical image is acquired per 0.25 mm2 area of the molybdenum disulfide sample.
Further, in the ROI segmentation in the step (4), an ROI of the optical image is segmented, scaled to a same pixel size as an AFM image, and further segmented.
Further, in the image feature extraction in the step (4), an effect of a light intensity of the segmented ROI on a color is reduced by
where L denotes a light intensity depth; A(L) denotes an optical compensation function; B, G, and R denote color feature values; and Lsilicon denotes a light intensity depth of a silicon wafer region.
Further, in the step (5), an effect of a characterization accuracy error of the AFM height data on the accuracy of the model after the training is reduced by
where H denotes a processed height dataset; and hn denotes an n-th original height data.
Further, in the dataset splitting in the step (6), a split ratio of the training set and the testing set is 4:1.
Further, in the 3D image filtering in the step (8), the height data are subjected to mean filtering by a 3*3 mask; and in the step (4), the segmented local region of the optical image has a pixel value of 500*500 pt.
The present disclosure further provides a model for 3D characterization of a molybdenum disulfide sample, which is constructed by the method for 3D characterization of the molybdenum disulfide sample based on ML.
The present disclosure further provides a use of the model for 3D characterization of the molybdenum disulfide sample in the 3D characterization of the molybdenum disulfide sample based on an optical image of the molybdenum disulfide sample.
The present disclosure has the following beneficial effects.
The present disclosure combines two-dimensional materials and the ML technology to perform 3D characterization of a molybdenum disulfide sample through optical imaging, and achieves the higher characterization accuracy. The present disclosure is helpful for scientific researchers to quickly analyze the thickness of a molybdenum disulfide sample through optical imaging without AFM or other characterization instrumentation. The present disclosure also makes a preliminary exploration on the method of 3D characterization of samples through optical imaging for scientific researchers in future.
The present disclosure is described in further detail below with reference to the drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present disclosure, rather than to limit the scope of the present disclosure. Those skilled in the art should understand that any equivalent modifications made to the present disclosure should fall within the scope defined by the claims of the present disclosure.
As shown in
An optical image is acquired by a microscope. The molybdenum disulfide sample is prepared by a micromechanical exfoliation method using a 1*1 cm, 300 nm heavily doped P-type silicon oxide wafer as a substrate. The substrate is first heated in acetone for 10 min and ultrasonically cleaned for 10 min, and then ultrasonically cleaned by isoethanol for 5 min. Residual acetone is then removed. The substrate is rinsed with deionized water and blown dry with nitrogen gas, such that a surface of the substrate is clean. The molybdenum disulfide sample is prepared by a micromechanical exfoliation method and a bulk molybdenum disulfide sample is taken by a Nitto tape, and the sample is sufficiently thinned by tearing it 3-6 times. The tape with the molybdenum disulfide sample is picked up by using tweezers, and the sample is pressed against the cleaned silicon oxide wafer with a finger, and air bubbles therebetween are squeezed out to make the molybdenum disulfide sample fully adhere to the silicon oxide wafer. Finally, the tape is removed to acquire the final molybdenum disulfide sample. The optical image is acquired under a linearly adjustable light source, and one optical image is acquired per 0.25 mm2 area of the molybdenum disulfide sample. The acquired optical image is subjected to denoising and mean filtering.
AFM characterization is performed in the same local region for the optical image acquisition to acquire AFM height data of the local region of the molybdenum disulfide sample, as shown in
Specifically, in the image feature extraction, an effect of a light intensity of the segmented ROI on a color is reduced by
where L denotes a light intensity depth; A(L) denotes an optical compensation function; B, G, and R denote color feature values; and Lsilicon denotes a light intensity depth of a silicon wafer region. Through such processing, a final color feature value of the molybdenum disulfide sample is acquired. An effect of a characterization accuracy error of the AFM height data on the accuracy of the model after the training is reduced by
where H denotes a processed height dataset; and hn denotes an n-th original height data.
Then, feature dataset splitting and ML model training are conducted. The feature dataset is split into a training set and a testing set, and the training set is used for training a model, and the testing set is used for validating an accuracy of the model. A split ratio of the training set and the testing set is 4:1.
The model is constructed by a random forest (RF) algorithm based on the training set, and trained by controlling a number of random trees based on the testing set, so as to improve the accuracy of the model. Finally, the model is exported.
The steps of optical image acquisition and image processing are performed on a target molybdenum disulfide sample to obtain an optical image. A color feature value of the optical image of the sample is extracted, and brought into the exported model to calculate height data of the target molybdenum disulfide sample. An acquired 3D image is filtered by a 3*3 mask to remove a local noise and a local abnormal point, so as to acquire a final 3D characterization image, as shown in
The above embodiments are preferred implementations of the present disclosure, but the present disclosure is not limited to the above implementations. Any obvious improvement, substitution, or modification made by those skilled in the art without departing from the essence of the present disclosure should fall within the protection scope of the present disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202110457805.9 | Apr 2021 | CN | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CN2021/120532 | 9/26/2021 | WO |
