CN113283301A - 一种基于机器学习的单层二硫化钼样品光学表征方法、模型及其用途 - Google Patents
一种基于机器学习的单层二硫化钼样品光学表征方法、模型及其用途 Download PDFInfo
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Abstract
本发明提供了一种基于机器学习的单层二硫化钼样品光学表征方法、模型及其用途。首先,将二硫化钼样品光学成像通过图形处理提取出可疑单层ROI区域;然后,将可疑单层ROI局域的像素值与硅片在光学显微镜下拍摄出来的特征值求差值向量,通过拉曼表征来区分单层和少层样品,通过肉眼观测的方式确定残胶,根据层数分类来建立目标值;将差值向量求平均值和标准值作为特征值,并与目标值组成数据集,最后通过对数据集降维并通过机器学习算法对该数据集进行分类,获得最佳单层表征模型。基于该模型,通过光学成像即可快速分辨出单层二硫化钼样品,极大的节约寻找单层二硫化钼所需要花费的时间。
Description
技术领域
本发明属于二维材料检测应用领域,涉及基于机器学习的单层二硫化钼样品光学表征方法、模型及其用途,对二硫化钼进行单层识别。
背景技术
随着新型二维材料的不断发现,二维材料优异的物理性能受到广泛国内外科研人员的关注。单层二硫化钼晶体管的电子迁移率最高可达约500cm2/(V·s),电流开关率达到1×108等特性,使其成为科研人员重点研究的材料,然而化学气相沉积生长的单层二硫化钼容易出现缺陷并引入杂质,严重影响制备器件的物理性能。自然界存在的二硫化钼块体质地均匀、杂质少、性能优异等因素成为科研工作者热衷的材料来源。微机械剥离法操作简便,是目前制备单层样品最有效方法之一。二硫化钼在纳米晶体管领域拥有很广阔的应用空间。由于单层二硫化钼样品非常薄,在光学成像中往往与背景硅片颜色十分接近,导致科研工作者寻找单层的二硫化钼样品耗时较长,而且在寻找到类似单层的样品后还需要通过拉曼表征来进一步验证是否为单层,这样一种方式比较麻烦。近年来,机器学习技术发展越来越快,机器学习的判断准确率也已超过了人的判断能力,虽然技术的进一步发展,但是机器学习在二维材料领域的应用依然较少,主要问题是找不到合适的样品特征提取方法,因此本文发明了一种针对单层二硫化钼的光学表征的方法。
发明内容
针对现有技术中存在不足,本发明提供了一种基于机器学习的单层二硫化钼样品光学表征方法、模型及其用途,克服单层的二硫化钼检测过程耗时长、困难的问题。
本发明是通过以下技术手段实现上述技术目的的。
一种基于机器学习的单层二硫化钼样品光学表征方法,其特征在于,包括以下步骤:
(1)光学图像采集:制备承载在硅片上的单层二硫化钼样品,将二硫化钼样品置于显微镜下通过电镜拍摄图像;
(2)图像处理:将拍摄的二硫化钼样品图像进行图像处理,寻找疑似单层的ROI区域;
(3)图像像素差值特征提取:将疑似单层的ROI区域像素颜色特征值减去硅片颜色特征值得到差值向量;
(4)拉曼表征:将疑似单层的ROI区域二硫化钼样品进行拉曼表征,来区分单层和少层样品,根据层数分类来建立目标值;
(5)建立数据集:将步骤(3)中提取出来的差值向量求平均值和标准值作为特征值,并与步骤(4)获得的目标值组成数据集;
(6)数据集降维:将形成的数据集降维,并将数据集划分为训练集和测试集;
(7)机器学习模型训练:将训练集和测试集分别代入机器学习SVM算法中训练获得检测模型。
进一步地,所述步骤(1)中光学图像采集是通过显微镜采集,一幅图像采集样品区域面积为0.25mm2,采集光源为线性可调光源,光源稳定且温漂低。
进一步地,所述步骤(2)中图像处理是:首先通过高斯滤波滤除图像噪声,然后通过单通道分离图像将滤波后的图像的通道分离获得单层图像分离效果最好的图像;再对图像采用灰度图像直方图找到图像中的硅片像素特征值,再将灰度图像转换为二值化图像,最后对图像进行形态学开操作,滤除高斯滤波带来的类似单层的特征区域,即滤除高斯滤波后造成的假单层边缘;将上述图像通过Canny边缘检测获得的疑似样品区域标记为ROI区域,并将该边缘标记在原始图像上。
进一步地,步骤(2)中经过图像处理后所获得的疑似单层的ROI区域是单层、少层或残胶,还包括剔除残胶区域的步骤。
进一步地,步骤(5)建立数据集中,特征值数据集由图像差值特征做平均值和方差获得最终的ROI区域特征的向量:目标数据集是通过肉眼分辨出大片残胶标记目标值为0,通过拉曼确认单层样品标记目标值是1,其余样品标记为2。
所述的基于机器学习的单层二硫化钼样品光学表征方法所建立的检测模型。
所述检测模型用于筛选单层二硫化钼样品。
本发明的有益效果是:本发明采用二维材料与机器学习的方法相结合,通过光学成像来对单层二硫化钼样品进行表征,表征精度高,有助于科研人员在没有拉曼等仪器的情况下,通过光学成像快速分辨出单层二硫化钼样品,极大的节约了科研工作者在寻找单层二硫化钼所需要花费的时间,也为未来科研工作者在光学对光学表征二维材料做出来贡献。
附图说明
图1为本发明所述机器学习的单层二硫化钼样品光学表征方法的流程图。
图2为光学显微镜下二硫化钼样品。
图3为图2中的图像经过单通道分离处理后获得的灰度图。
图4为标记的疑似单层的ROI区域。
具体实施方式
下面结合附图以及具体实施例对本发明作进一步的说明,但本发明的保护范围并不限于此。
单层二维材料在光学显微镜下拍摄出来的图像颜色非常接近背景硅片颜色,较厚的样品在显微镜下成像颜色区分度较高,但是少层样品及残胶颜色也接近硅片颜色特征。本发明就是基于该事实所做出的。
图1所示是本发明所述基于机器学习的单层二硫化钼样品光学表征方法的流程图,主要包括光学图像采集、图像处理、寻找ROI区域、拉曼表征、图像像素差值特征提取、建立数据集、数据集划分、数据集降维、机器学习模型训练等步骤。
(1)光学图像采集
首先,制备承载在硅片上的单层二硫化钼样品,将二硫化钼样品置于显微镜下通过电镜拍摄图像。单层二硫化钼样品的制备过程如下:先超声波清洗硅片,通过微机械剥离法制备单层二硫化钼样品,再将二硫化钼样品转移到硅片上。
微机械剥离法制备单层二硫化钼的原理是通过微机械剥离法重复撕几次,可以使得二硫化钼逐渐变薄从而可能存在单层样品。衬底选用1*1cm的P型重掺300nm氧化硅片,将衬底先采用加入丙酮热10min超声清洗10min,然后用异乙醇超声清洗5min,去除残余的丙酮,最后用去离子水清洗并用氮气吹干,使衬底表面清洁。通过微机械剥离法制备二硫化钼样品,采用Nitto胶带粘取二硫化钼块体样品后,将将粘取的样品重复撕开3-6次使得样品充分变薄。用镊子夹取含有二硫化钼样品的胶带并用手指将样品按压在清洗好的硅片上,并挤出中间气泡,使得二硫化钼样品充分附着在氧化硅片上。将胶带撕去,得到最终的二硫化钼样品。
其中,光学图像采集通过显微镜采集,一幅图像采集样品区域面积为0.25mm2样品,采集光源为线性可调光源,光源稳定且温漂低。
(2)图像处理:将拍摄的二硫化钼样品图像进行图像处理,寻找疑似单层的ROI区域;
图像处理由高斯滤波、单通道分离图像、灰度图像直方图、二值化图像、形态学开操作处理图、ROI可疑区域标记组成。首先通过高斯滤波滤除图像噪声,然后通过单通道分离图像将滤波后的图像的通道分离获得单层图像分离效果最好的图像;再对图像采用灰度图像直方图找到图像中的硅片像素特征值,再将灰度图像转换为二值化图像,最后对图像进行形态学开操作,滤除高斯滤波带来的类似单层的特征区域,即滤除高斯滤波后造成的假单层边缘;将上述图像通过Canny边缘检测获得的疑似样品区域标记为ROI区域,并将该边缘标记在原始图像上。
经过图像处理后所获得的疑似单层的ROI区域是单层、少层或残胶,还包括剔除残胶区域的步骤。
(4)拉曼表征:将疑似单层的ROI区域二硫化钼样品进行拉曼表征,来区分单层和少层样品,根据层数分类来建立目标值;
(5)建立数据集:将步骤(3)中提取出来的差值向量求平均值和标准值作为特征值,并与步骤(4)获得的目标值组成数据集。
(6)数据集降维:将形成的数据集降维,减少计算次数。
(7)机器学习模型训练:将数据集划分为训练集和测试集,将训练集和测试集分别代入机器学习SVM算法中训练获得检测模型,提高学习准确率,训练出最终单层二硫化钼的检测模型。
所述实施例为本发明的优选的实施方式,但本发明并不限于上述实施方式,在不背离本发明的实质内容的情况下,本领域技术人员能够做出的任何显而易见的改进、替换或变型均属于本发明的保护范围。
Claims (9)
1.一种基于机器学习的单层二硫化钼样品光学表征方法,其特征在于,包括以下步骤:
(1)光学图像采集:制备承载在硅片上的单层二硫化钼样品,将二硫化钼样品置于显微镜下通过电镜拍摄图像;
(2)图像处理:将拍摄的二硫化钼样品图像进行图像处理,寻找疑似单层的ROI区域;
(3)图像像素差值特征提取:将疑似单层的ROI区域像素颜色特征值减去硅片颜色特征值得到差值向量;
(4)拉曼表征:将疑似单层的ROI区域二硫化钼样品进行拉曼表征,来区分单层和少层样品,根据层数分类来建立目标值;
(5)建立数据集:将步骤(3)中提取出来的差值向量求平均值和标准值作为特征值,并与步骤(4)获得的目标值组成数据集;
(6)数据集降维:将形成的数据集降维;
(7)机器学习模型训练:将数据集划分为训练集和测试集,将训练集和测试集分别代入机器学习SVM算法中训练获得检测模型。
2.根据权利要求1所述的基于机器学习的单层二硫化钼样品光学表征方法,其特征在于,所述步骤(1)中光学图像采集是通过显微镜采集,一幅图像采集样品区域面积为0.25mm2,采集光源为线性可调光源。
3.根据权利要求1所述的基于机器学习的单层二硫化钼样品光学表征方法,其特征在于,所述步骤(2)中图像处理是:首先通过高斯滤波滤除图像噪声,然后通过单通道分离图像将滤波后的图像的通道分离获得单层图像分离效果最好的图像;再对图像采用灰度图像直方图找到图像中的硅片像素特征值,再将灰度图像转换为二值化图像,最后对图像进行形态学开操作,滤除高斯滤波带来的类似单层的特征区域,即滤除高斯滤波后造成的假单层边缘;将上述图像通过Canny边缘检测获得的疑似样品区域标记为ROI区域,并将该边缘标记在原始图像上。
5.根据权利要求1-4中任一项所述的基于机器学习的单层二硫化钼样品光学表征方法,其特征在于,步骤(2)中经过图像处理后所获得的疑似单层的ROI区域是单层、少层或残胶,还包括剔除残胶区域的步骤。
8.权利要求1-7中任一项所述的基于机器学习的单层二硫化钼样品光学表征方法所建立的检测模型。
9.权利要求8所述检测模型用于筛选单层二硫化钼样品。
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