In this work,an old scanning electron microscope(SEM)is refurbished to enhance its image processing capability.How to digitally sample and process an analog image is also presented.An NI PCI-6259 multiple input/output...In this work,an old scanning electron microscope(SEM)is refurbished to enhance its image processing capability.How to digitally sample and process an analog image is also presented.An NI PCI-6259 multiple input/output data acquisition(DAQ)board is used to acquire signals originally being sent to an analog display,and then convert the signals into a digital image.Two output channels are used for raster scan of the horizontal and verticle axes of the image buffer,while one input channel is used to read the brightness signals at various coordinate points.Synchronous method is used to maximize the DAQ speed.Finally,the digitally buffered images are read out to display and saved in a hard drive.The hardware and software designs of this work are explained in great detail,which can serve as a very good example for fast synchronous DAQ,advanced virtual instrument design and structural driver programming with LabVIEW.展开更多
Scanning electron acoustic microscopy (SEAM) is a new technique for imasing and characterization ofthermal, elastic and pyroelectric property variations on a microscale resolution. The signal generation mechanisms and...Scanning electron acoustic microscopy (SEAM) is a new technique for imasing and characterization ofthermal, elastic and pyroelectric property variations on a microscale resolution. The signal generation mechanisms and the application of scanning electron acoustic microscopy in GalnAsSb alloy grown by MOCVD wereinvestigated. Defects below the surface of GalnAsSb alloy were found by SEAM images and cathodelumi-nescence. The results show that electronacoustic imaging has its own features over secondary electron imag-ing.展开更多
A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in de...A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in defect recognition. Seven features were extracted from the image and represented 87. 3% information of the original data. Both the extracted features and the original data were used to train support vector machine model to assess the feature extraction performance in two aspects: recognition accuracy and training time. The results show that using the extracted features the recognition accuracy of pore,crack,lack of fusion and lack of penetration are 93%,90.7%,94.7% and 89.3%,respectively,which is slightly higher than those using the original data. The training time of the models using the extracted features is extremely reduced comparing with those using the original data.展开更多
Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for th...Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for thermal imaging and transport research.Here,we introduce an approach to measure nanoscale thermal resistance using in situ inelastic scanning transmission electron microscopy.By constructing unidirectional heating flux with controlled temperature gradients and analyzing electron energy-loss/gain signals under optimized acquisition conditions,nanometer-resolution in mapping phonon apparent temperature is achieved.Thus,interfacial thermal resistance is determined by calculating the ratio of interfacial temperature difference to bulk temperature gradient.This methodology enables direct measurement of thermal transport properties for atomic-scale structural features(e.g.,defects and heterointerfaces),resolving critical structure-performance relationships,providing a useful tool for investigating thermal phenomena at the(sub-)nanoscale.展开更多
Three-dimensional organic-inorganic hybrid perovskites(OHPs)hold a great prospect for photovoltaic applications due to their outstanding electronic and optical properties.These fascinating properties of OHPs in combin...Three-dimensional organic-inorganic hybrid perovskites(OHPs)hold a great prospect for photovoltaic applications due to their outstanding electronic and optical properties.These fascinating properties of OHPs in combination with their scalable and low-cost production make OHPs promising candidates for next-generation optoelectronic devices.The ability to obtain atomistic insights into physicochemical properties of this class of materials is crucial for the future development of this field.Recent advances in various scanning probe microscopy techniques have demonstrated their extraordinary capability in real-space imaging and spectroscopic measurements of the structural and electronic properties of OHPs with atomic-precision.Moreover,these techniques can be combined with light illumination to probe the structural and optoelectronic properties of OHPs close to the real device operation conditions.The primary focus of this review is to summarize the recent progress in atomic-scale studies of OHPs towards a deep understanding of the phenomena discovered in OHPs and OHP-based optoelectronic devices.展开更多
Scanning electron microscopy(SEM)enables nanoscale imaging but requires vacuum environments and coating samples with conductive films.We present a deep learning approach to transform optical super-resolution(OSR)micro...Scanning electron microscopy(SEM)enables nanoscale imaging but requires vacuum environments and coating samples with conductive films.We present a deep learning approach to transform optical super-resolution(OSR)microscopy images into high-resolution images resembling SEM,specifically optimized for chip samples.Utilizing our custom-designed scanning superlens microscopy(SSUM)system,we acquire OSR images with a resolution down to~80 nm without the need for coatings or vacuum conditions.Notably,the SSUM system achieves an effective depthof-field(DoF)of approximately 2μm through Z-stack scanning,enabling clear visualization of multilayer chip structures across a larger axial range than conventional optical imaging.Our algorithm further enhances the nanoscale microstructures observed with the SSUM platform,significantly improving the visibility of structures that are otherwise less distinct.A cycle-consistent generative adversarial network(CycleGAN)model is trained on paired OSR and SEM images to learn the mapping between these imaging modalities.The model is then applied to unseen OSR test images from silicon wafer samples.Quantitative analysis shows that the reconstructed images achieve a mean peak signal-to-noise ratio(PSNR)1.64 dB higher than the input OSR images.Qualitative assessment further demonstrates the model’s ability to generate results with high structural detail,specifically in chip-level applications.This technique overcomes key SEM constraints while preserving nanoscale resolution,offering the potential for advanced chip manufacturing and inspection tasks where traditional SEM requirements pose challenges.展开更多
Aberration-corrected annular dark-field scanning transmission electron microscopy(ADF-STEM)is a powerful tool for structural and chemical analysis of materials.Conventional analyses of ADF-STEM images rely on human la...Aberration-corrected annular dark-field scanning transmission electron microscopy(ADF-STEM)is a powerful tool for structural and chemical analysis of materials.Conventional analyses of ADF-STEM images rely on human labeling,making them labor-intensive and prone to subjective error.Here,we introduce a deep-learning-based workflow combining a pix2pix network for image denoising and either a mathematical algorithm local intensity threshold segmentation(LITS)or another deep learning network UNet for chemical identification.After denoising,the processed images exhibit a five-fold improvement in signal-to-noise ratio and a 20%increase in accuracy of atomic localization.Then,we take atomic-resolution images of Y–Ce dual-atom catalysts(DACs)and Fe-doped ReSe_(2) nanosheets as examples to validate the performance.Pix2pix is applied to identify atomic sites in Y–Ce DACs with a location recall of 0.88 and a location precision of 0.99.LITS is used to further differentiate Y and Ce sites by the intensity of atomic sites.Furthermore,pix2pix and UNet workflow with better automaticity is applied to identification of Fe-doped ReSe_(2) nanosheets.Three types of atomic sites(Re,the substitution of Fe for Re,and the adatom of Fe on Re)are distinguished with the identification recall of more than 0.90 and the precision of higher than 0.93.These results suggest that this strategy facilitates high-quality and automated chemical identification of atomic-resolution images.展开更多
Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy.Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for...Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy.Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments.Here,we present an automated method of correcting first-and second-order aberrations called BEACON,which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings.We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano-and atomic-scale experiments.BEACON can correct all firstand second-order aberrations simultaneously to achieve an initial alignment and first-and secondorder aberrations independently for fine alignment.Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.展开更多
光电关联显微镜技术(Correlative light and electron microscopy, CLEM)将光学显微镜的颜色分辨能力和大视场与电子显微镜的高分辨率相结合,弥补了各自成像的局限,能获得更全面准确的定位及结构信息。本文提出了一种基于超景深光学显...光电关联显微镜技术(Correlative light and electron microscopy, CLEM)将光学显微镜的颜色分辨能力和大视场与电子显微镜的高分辨率相结合,弥补了各自成像的局限,能获得更全面准确的定位及结构信息。本文提出了一种基于超景深光学显微镜与场发射扫描电子显微镜(SEM)的光电关联与样品定位技术,用于解决SEM在样品定位过程中效率低、耗时长的问题。通过超景深光学显微镜的快速全景成像与颜色识别能力,结合高精度坐标转换算法,实现了目标区域的快速定位与SEM的高分辨率成像。实验结果表明,该技术显著提高了样品定位效率,大大缩短了定位时间,同时保持了良好的定位与成像精度。本研究为材料科学、生命科学等领域的大尺寸或复杂颜色分布样品的快速分析提供了有效解决方案。展开更多
The coarse pore system, interfacial transition zone （ITZ） between aggregate and paste matrix and volume fraction of unhydrated cement in concrete （w/c=0.3） containing mineral admixtures were quantitatively charact...The coarse pore system, interfacial transition zone （ITZ） between aggregate and paste matrix and volume fraction of unhydrated cement in concrete （w/c=0.3） containing mineral admixtures were quantitatively characterized by the scanning electron microscope-backscattered electron （SEM-BSE） image analysis technique. The experimental results show that compound addition of slag and fly ash decreases the coarse porosity from 10.17% to 3.74% and the threshold diameter of coarse pore size from 345 μm to 105 μm compared with concrete （w/c=0.30） without mineral admixtures; Moreover with compound addition of fly ash and slag, the volume proportion of unhydrated cement in paste matrix is reduced by 30%, the maximum amount of coarse pores in the ITZ between aggregate and paste decreases from 13.11% to 5.57% and the thickness of ITZ is reduced by 37% , compared with concrete without mineral admixtures.展开更多
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data...This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.展开更多
扫描电子显微镜(Scanning Electron Microscope, SEM)的图像质量高度依赖于扫描电路输出信号的精度与稳定性。而现有研究对扫描信号至图像质量的影响仍缺乏深入分析。基于此,首先建立了从扫描信号到图像质量的传递机制,分析了扫描信号...扫描电子显微镜(Scanning Electron Microscope, SEM)的图像质量高度依赖于扫描电路输出信号的精度与稳定性。而现有研究对扫描信号至图像质量的影响仍缺乏深入分析。基于此,首先建立了从扫描信号到图像质量的传递机制,分析了扫描信号性能指标对图像影响;然后,通过高精度数字万用表与示波器对实际扫描信号进行精准测量,并结合端点拟合法进行数据处理,从而量化了场扫描与行扫描信号在512×512像素模式下误差水平;进一步通过MATLAB仿真各类误差对标准网格图像的影响,并导入实测信号数据,评估其在实际成像中的表现。研究表明,扫描信号整体线性性能良好,中心区域平均误差为1.884像素,优于边缘区域的2.749像素。展开更多
页岩扫描电镜(scanning electron microscope,SEM)图像智能识别能够快速分析页岩储层矿物,是页岩油储层“甜点”预测的重要手段之一,也是未来的技术发展趋势。传统方法在鉴定矿物成分时存在自动化程度低、样本适配度低和特征提取受限等...页岩扫描电镜(scanning electron microscope,SEM)图像智能识别能够快速分析页岩储层矿物,是页岩油储层“甜点”预测的重要手段之一,也是未来的技术发展趋势。传统方法在鉴定矿物成分时存在自动化程度低、样本适配度低和特征提取受限等问题。为此,本文提出基于BlendMask的页岩SEM图像鉴定方法。首先,采用双边滤波、拉普拉斯和图像归一化等图像预处理技术对原始图像进行去噪、锐化和像素统一处理,提高训练样本的质量;然后,采用旋转、缩放、光度变化等图像增广方法构建增广策略,扩大数据集数量;最后,利用注意力机制和深度可分离卷积改进BlendMask网络,实现图像的成分分割与识别。应用于海塔盆地的页岩SEM图像实验结果表明,相比BlendMask模型,改进后方法的分割准确率和召回率分别提升了0.02~0.20和0~0.59,分割用时减少了1.29~2.70 s。展开更多
文摘In this work,an old scanning electron microscope(SEM)is refurbished to enhance its image processing capability.How to digitally sample and process an analog image is also presented.An NI PCI-6259 multiple input/output data acquisition(DAQ)board is used to acquire signals originally being sent to an analog display,and then convert the signals into a digital image.Two output channels are used for raster scan of the horizontal and verticle axes of the image buffer,while one input channel is used to read the brightness signals at various coordinate points.Synchronous method is used to maximize the DAQ speed.Finally,the digitally buffered images are read out to display and saved in a hard drive.The hardware and software designs of this work are explained in great detail,which can serve as a very good example for fast synchronous DAQ,advanced virtual instrument design and structural driver programming with LabVIEW.
文摘Scanning electron acoustic microscopy (SEAM) is a new technique for imasing and characterization ofthermal, elastic and pyroelectric property variations on a microscale resolution. The signal generation mechanisms and the application of scanning electron acoustic microscopy in GalnAsSb alloy grown by MOCVD wereinvestigated. Defects below the surface of GalnAsSb alloy were found by SEAM images and cathodelumi-nescence. The results show that electronacoustic imaging has its own features over secondary electron imag-ing.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.51575134 and 51205083)
文摘A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in timeconsuming in defect recognition. Seven features were extracted from the image and represented 87. 3% information of the original data. Both the extracted features and the original data were used to train support vector machine model to assess the feature extraction performance in two aspects: recognition accuracy and training time. The results show that using the extracted features the recognition accuracy of pore,crack,lack of fusion and lack of penetration are 93%,90.7%,94.7% and 89.3%,respectively,which is slightly higher than those using the original data. The training time of the models using the extracted features is extremely reduced comparing with those using the original data.
基金supported by the National Natural Science Foundation of China(Grant No.52125307)the National Key R&D Program of China(Grant No.2021YFB3501500)the support from the New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for thermal imaging and transport research.Here,we introduce an approach to measure nanoscale thermal resistance using in situ inelastic scanning transmission electron microscopy.By constructing unidirectional heating flux with controlled temperature gradients and analyzing electron energy-loss/gain signals under optimized acquisition conditions,nanometer-resolution in mapping phonon apparent temperature is achieved.Thus,interfacial thermal resistance is determined by calculating the ratio of interfacial temperature difference to bulk temperature gradient.This methodology enables direct measurement of thermal transport properties for atomic-scale structural features(e.g.,defects and heterointerfaces),resolving critical structure-performance relationships,providing a useful tool for investigating thermal phenomena at the(sub-)nanoscale.
基金support from MOE Tier 2 grants (MOE2017T2-1-056, MOE2016-T2-2-020 and R-143-000-A75-114)
文摘Three-dimensional organic-inorganic hybrid perovskites(OHPs)hold a great prospect for photovoltaic applications due to their outstanding electronic and optical properties.These fascinating properties of OHPs in combination with their scalable and low-cost production make OHPs promising candidates for next-generation optoelectronic devices.The ability to obtain atomistic insights into physicochemical properties of this class of materials is crucial for the future development of this field.Recent advances in various scanning probe microscopy techniques have demonstrated their extraordinary capability in real-space imaging and spectroscopic measurements of the structural and electronic properties of OHPs with atomic-precision.Moreover,these techniques can be combined with light illumination to probe the structural and optoelectronic properties of OHPs close to the real device operation conditions.The primary focus of this review is to summarize the recent progress in atomic-scale studies of OHPs towards a deep understanding of the phenomena discovered in OHPs and OHP-based optoelectronic devices.
基金supported by the Hong Kong Research Grants Council(Project No.11216120)the Science,Technology,and Innovation Commission of Shenzhen Municipality(Grant Nos.SGDX2019081623121725 and JCYJ20190808181803703).
文摘Scanning electron microscopy(SEM)enables nanoscale imaging but requires vacuum environments and coating samples with conductive films.We present a deep learning approach to transform optical super-resolution(OSR)microscopy images into high-resolution images resembling SEM,specifically optimized for chip samples.Utilizing our custom-designed scanning superlens microscopy(SSUM)system,we acquire OSR images with a resolution down to~80 nm without the need for coatings or vacuum conditions.Notably,the SSUM system achieves an effective depthof-field(DoF)of approximately 2μm through Z-stack scanning,enabling clear visualization of multilayer chip structures across a larger axial range than conventional optical imaging.Our algorithm further enhances the nanoscale microstructures observed with the SSUM platform,significantly improving the visibility of structures that are otherwise less distinct.A cycle-consistent generative adversarial network(CycleGAN)model is trained on paired OSR and SEM images to learn the mapping between these imaging modalities.The model is then applied to unseen OSR test images from silicon wafer samples.Quantitative analysis shows that the reconstructed images achieve a mean peak signal-to-noise ratio(PSNR)1.64 dB higher than the input OSR images.Qualitative assessment further demonstrates the model’s ability to generate results with high structural detail,specifically in chip-level applications.This technique overcomes key SEM constraints while preserving nanoscale resolution,offering the potential for advanced chip manufacturing and inspection tasks where traditional SEM requirements pose challenges.
基金supported by the National Key Research and Development Program of China(2022YFA1505700)National Natural Science Foundation of China(22475214 and 22205232)+2 种基金Talent Plan of Shanghai Branch,Chinese Academy of Sciences(CASSHB-QNPD-2023-020)Natural Science Foundation of Fujian Province(2023J06044)the Self-deployment Project Research Program of Haixi Institutes,Chinese Academy of Sciences(CXZX-2022-JQ06 and CXZX-2022-GH03)。
文摘Aberration-corrected annular dark-field scanning transmission electron microscopy(ADF-STEM)is a powerful tool for structural and chemical analysis of materials.Conventional analyses of ADF-STEM images rely on human labeling,making them labor-intensive and prone to subjective error.Here,we introduce a deep-learning-based workflow combining a pix2pix network for image denoising and either a mathematical algorithm local intensity threshold segmentation(LITS)or another deep learning network UNet for chemical identification.After denoising,the processed images exhibit a five-fold improvement in signal-to-noise ratio and a 20%increase in accuracy of atomic localization.Then,we take atomic-resolution images of Y–Ce dual-atom catalysts(DACs)and Fe-doped ReSe_(2) nanosheets as examples to validate the performance.Pix2pix is applied to identify atomic sites in Y–Ce DACs with a location recall of 0.88 and a location precision of 0.99.LITS is used to further differentiate Y and Ce sites by the intensity of atomic sites.Furthermore,pix2pix and UNet workflow with better automaticity is applied to identification of Fe-doped ReSe_(2) nanosheets.Three types of atomic sites(Re,the substitution of Fe for Re,and the adatom of Fe on Re)are distinguished with the identification recall of more than 0.90 and the precision of higher than 0.93.These results suggest that this strategy facilitates high-quality and automated chemical identification of atomic-resolution images.
基金funded by the US Department of Energy in the program "Electron Distillery 2.0: Massive Electron Microscopy Data to Useful Information with AI/ML."Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231+1 种基金This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award BES-ERCAP0028035S.M.R. and C.O. acknowledge support from the U.S. Department of Energy Early Career Research Award program. J.P. acknowledges financial support from the National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (Grant No. RS-2023-00283902, and RS-202400408823). We gratefully acknowledge CEOS, GmbH for providing the server enabling communication with the corrector and I. Massmann for technical assistance.
文摘Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy.Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments.Here,we present an automated method of correcting first-and second-order aberrations called BEACON,which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings.We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano-and atomic-scale experiments.BEACON can correct all firstand second-order aberrations simultaneously to achieve an initial alignment and first-and secondorder aberrations independently for fine alignment.Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.
文摘光电关联显微镜技术(Correlative light and electron microscopy, CLEM)将光学显微镜的颜色分辨能力和大视场与电子显微镜的高分辨率相结合,弥补了各自成像的局限,能获得更全面准确的定位及结构信息。本文提出了一种基于超景深光学显微镜与场发射扫描电子显微镜(SEM)的光电关联与样品定位技术,用于解决SEM在样品定位过程中效率低、耗时长的问题。通过超景深光学显微镜的快速全景成像与颜色识别能力,结合高精度坐标转换算法,实现了目标区域的快速定位与SEM的高分辨率成像。实验结果表明,该技术显著提高了样品定位效率,大大缩短了定位时间,同时保持了良好的定位与成像精度。本研究为材料科学、生命科学等领域的大尺寸或复杂颜色分布样品的快速分析提供了有效解决方案。
文摘The coarse pore system, interfacial transition zone （ITZ） between aggregate and paste matrix and volume fraction of unhydrated cement in concrete （w/c=0.3） containing mineral admixtures were quantitatively characterized by the scanning electron microscope-backscattered electron （SEM-BSE） image analysis technique. The experimental results show that compound addition of slag and fly ash decreases the coarse porosity from 10.17% to 3.74% and the threshold diameter of coarse pore size from 345 μm to 105 μm compared with concrete （w/c=0.30） without mineral admixtures; Moreover with compound addition of fly ash and slag, the volume proportion of unhydrated cement in paste matrix is reduced by 30%, the maximum amount of coarse pores in the ITZ between aggregate and paste decreases from 13.11% to 5.57% and the thickness of ITZ is reduced by 37% , compared with concrete without mineral admixtures.
基金funded by the National Natural Science Foundation of China(No.52204407)the Natural Science Foundation of Jiangsu Province(No.BK20220595)the China Postdoctoral Science Foundation(No.2022M723689).
文摘This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.
文摘扫描电子显微镜(Scanning Electron Microscope, SEM)的图像质量高度依赖于扫描电路输出信号的精度与稳定性。而现有研究对扫描信号至图像质量的影响仍缺乏深入分析。基于此,首先建立了从扫描信号到图像质量的传递机制,分析了扫描信号性能指标对图像影响;然后,通过高精度数字万用表与示波器对实际扫描信号进行精准测量,并结合端点拟合法进行数据处理,从而量化了场扫描与行扫描信号在512×512像素模式下误差水平;进一步通过MATLAB仿真各类误差对标准网格图像的影响,并导入实测信号数据,评估其在实际成像中的表现。研究表明,扫描信号整体线性性能良好,中心区域平均误差为1.884像素,优于边缘区域的2.749像素。
文摘页岩扫描电镜(scanning electron microscope,SEM)图像智能识别能够快速分析页岩储层矿物,是页岩油储层“甜点”预测的重要手段之一,也是未来的技术发展趋势。传统方法在鉴定矿物成分时存在自动化程度低、样本适配度低和特征提取受限等问题。为此,本文提出基于BlendMask的页岩SEM图像鉴定方法。首先,采用双边滤波、拉普拉斯和图像归一化等图像预处理技术对原始图像进行去噪、锐化和像素统一处理,提高训练样本的质量;然后,采用旋转、缩放、光度变化等图像增广方法构建增广策略,扩大数据集数量;最后,利用注意力机制和深度可分离卷积改进BlendMask网络,实现图像的成分分割与识别。应用于海塔盆地的页岩SEM图像实验结果表明,相比BlendMask模型,改进后方法的分割准确率和召回率分别提升了0.02~0.20和0~0.59,分割用时减少了1.29~2.70 s。
