Atomic force microscopy(AFM)probe vibration monitoring is essential for achieving accurate nanoscale imaging and reliable signal interpretation.This paper presents a low-noise vibration detection method based on a rea...Atomic force microscopy(AFM)probe vibration monitoring is essential for achieving accurate nanoscale imaging and reliable signal interpretation.This paper presents a low-noise vibration detection method based on a real-time FPGA-LabVIEW homebuilt system.The FPGA receives signals from a quadrant photodiode(QPD),performs analog-to-digital conversion and parallel processing,and integrates cascaded digital filters for noise reduction.A finite impulse response(FIR)low-pass filter extracts the static spot position,while an infinite impulse response(IIR)band-pass filter preserves the probe’s resonance vibrations.Compared with conventional analog detection,the proposed system reduces background noise by approximately 50%(measured as 50.23%),enhances the signal-to-noise ratio(SNR)from 15 dB to 20 dB,and maintains FPGA signal-processing latency below 5μs.This work demonstrates that the proposed real-time FPGA-LabVIEW AFM noise optimization system significantly improves signal-to-noise ratio and real-time performance,providing a practical solution for high-precision,low-noise AFM imaging.展开更多
With the development of nanofabrication technologies,decreasing structural sizes,feature miniaturization,three-dimensional stacking,and concurrent increasing dimension characterize the measurement tasks for nano-measu...With the development of nanofabrication technologies,decreasing structural sizes,feature miniaturization,three-dimensional stacking,and concurrent increasing dimension characterize the measurement tasks for nano-measuring systems.Atomic Force Microscopy(AFM)and Scanning Electron Microscopy(SEM)are the most used metrology methods in nanometrology.However,each of the techniques has its inherent strengths and limitations;no single technique can provide the full capabilities,such as resolution,accuracy,and speed,to tackle the challenges of increasingly complex measurement tasks in nanometrology.In this study,a hybrid metrology approach using an Artificial Neural Network(ANN)is proposed to combine the advantages of AFM and SEM for the accurate and efficient measurements of geometrical parameters.To improve measurement efficiency,an automated measurement process utilizing deep learning has also been proposed.AFM and SEM measurement models are established to simulate training data for the ANN.This network can predict geometrical parameters more accurately with high efficiency,which can be achieved through individual techniques.Finally,the effectiveness of this method is validated by exemplary measurements for the determination of step height and pitch.This proposed approach also provides a promising solution for the laboratory-to-fab transition of metrology for semiconductors,for which automation and hybrid metrology are necessary.展开更多
The mechanical properties of clayey soils are intimately linked to Bound Water Content(BWC).Microbially Induced Calcium Precipitation(MICP),as an emerging technology for slope treatment and foundation engineering,has ...The mechanical properties of clayey soils are intimately linked to Bound Water Content(BWC).Microbially Induced Calcium Precipitation(MICP),as an emerging technology for slope treatment and foundation engineering,has an unclear mechanism in the influence on BWC of soil.Therefore,quantitative analysis of the Thickness of the Bound Water Film(TBWF)—a direct microscale characterization of BWC—holds significant importance.To quantitatively analyze the influence of MICP technology on TBWF,this study proposes a TBWF prediction model based on soil mechanics theory and validates effectiveness through Atomic Force Microscopy(AFM)experiments.Taking Granite Residual Soil(GRS)as the research object,the study revealed that MICP technology significantly reduces TBWF:when the cementation solution concentration was 1.0 mol/L,TBWF decreased from 48.297 nm(untreated control)to 34.561 nm(1.0 mol/L treatment group),a reduction of 28.44%.Further investigations revealed that MICP treatment lowers the liquid limit moisture content of soil while increasing specific gravity,bound water density,and specific surface area.However,when the concentration exceeded 1.0 mol/L,TBWF rebounded due to suppressed urease activity.AFM experimental data showed high consistency with theoretical model predictions,verifying the model’s reliability.This study provides microscopic mechanism support for the application of MICP technology in geotechnical engineering fields such as landslide prevention and slope reinforcement,establishes a new method for quantitative analysis of the bound water film,and holds important significance for improving the effectiveness of geological disaster prevention and control.展开更多
【目的】探究温拌沥青混合料黏附机理,拓展温拌沥青与集料黏附性研究方法。【方法】使用原子力显微镜(atomic force microscope,AFM)测得分别加入Sasobit类温拌剂H01(质量分数分别为1.0%、3.0%、5.0%)和表面活性剂类温拌剂H02(质量分数...【目的】探究温拌沥青混合料黏附机理,拓展温拌沥青与集料黏附性研究方法。【方法】使用原子力显微镜(atomic force microscope,AFM)测得分别加入Sasobit类温拌剂H01(质量分数分别为1.0%、3.0%、5.0%)和表面活性剂类温拌剂H02(质量分数分别为0.3%、0.5%、0.7%)的温拌沥青试样黏附力,并经JKR(Johnson-Kendall-Roberts)模型和表面能理论将其转换为沥青表面能,计算沥青-花岗岩和沥青-玄武岩体系的无水、有水黏附功,并与水煮试验、水稳定性试验结果进行对比分析;对基于AFM测得的温拌沥青退针力曲线进行积分,得到黏附性指标G,并将其与沥青表面能进行相关性拟合。【结果】两类温拌剂的加入均降低了温拌沥青-集料体系的黏附功,温拌沥青-花岗岩体系的无水黏附功比温拌沥青-玄武岩体系的大,有水黏附功则相反;基于AFM求解得到的H01温拌沥青-玄武岩体系黏附功的变化趋势与水稳定性试验结果相同,而H02温拌沥青-玄武岩体系的则相反;黏附性指标G与沥青表面能之间具有较好的相关性。【结论】相比温拌沥青-玄武岩体系,温拌沥青-花岗岩体系更容易发生水损害;基于AFM求解沥青-玄武岩体系黏附功的方法适用于Sasobit类温拌沥青,不适用于表面活性剂类温拌沥青;本文提出的黏附性指标G为评价温拌沥青-集料体系黏附性提供了新的量化指标。展开更多
Fine particle detachment and subsequent migration can lead to severe pore plugging and consequent permeability decline.Therefore,it is crucial to quantify the critical condition when fine particle detachment occurs.Th...Fine particle detachment and subsequent migration can lead to severe pore plugging and consequent permeability decline.Therefore,it is crucial to quantify the critical condition when fine particle detachment occurs.The frequently observed deviations or even contradictions between experimental results and theoretical predictions of fines detachment arise from an insufficient understanding of adhesion force that can be highly influenced by salinity and temperature.To clarify the intrinsic influence of salinity and temperature on fines detachment,adhesion forces between carboxyl microspheres and hydrophilic silica substrates in an aqueous medium were measured at various salinities and tempera-tures using atomic force microscopy(AFM).The AFM-measured adhesion force decreases with increasing salinity or temperature.Trends of mean measured adhesion forces with temperature and salinity were compared with the DLVO and XDLVO theories.DLVO theory captured the trend with temperature via the impact of temperature on electric double layer interactions,whereas XDLVO theory captured the observed trend with salinity via the impact of salinity on the repulsive hydration force.Our results highlight the significance of hydration force in accurately predicting the fate of fines in porous media.展开更多
基金supported by the National Key R&D Program of China(No.2022YFC2204104)NSFC Projects of International Cooperation and Exchanges(No.62220106012)Key Lab of Quantum Sensing and Precision Measurement,Shanxi(No.201905D121001)。
摘要Atomic force microscopy(AFM)probe vibration monitoring is essential for achieving accurate nanoscale imaging and reliable signal interpretation.This paper presents a low-noise vibration detection method based on a real-time FPGA-LabVIEW homebuilt system.The FPGA receives signals from a quadrant photodiode(QPD),performs analog-to-digital conversion and parallel processing,and integrates cascaded digital filters for noise reduction.A finite impulse response(FIR)low-pass filter extracts the static spot position,while an infinite impulse response(IIR)band-pass filter preserves the probe’s resonance vibrations.Compared with conventional analog detection,the proposed system reduces background noise by approximately 50%(measured as 50.23%),enhances the signal-to-noise ratio(SNR)from 15 dB to 20 dB,and maintains FPGA signal-processing latency below 5μs.This work demonstrates that the proposed real-time FPGA-LabVIEW AFM noise optimization system significantly improves signal-to-noise ratio and real-time performance,providing a practical solution for high-precision,low-noise AFM imaging.
摘要With the development of nanofabrication technologies,decreasing structural sizes,feature miniaturization,three-dimensional stacking,and concurrent increasing dimension characterize the measurement tasks for nano-measuring systems.Atomic Force Microscopy(AFM)and Scanning Electron Microscopy(SEM)are the most used metrology methods in nanometrology.However,each of the techniques has its inherent strengths and limitations;no single technique can provide the full capabilities,such as resolution,accuracy,and speed,to tackle the challenges of increasingly complex measurement tasks in nanometrology.In this study,a hybrid metrology approach using an Artificial Neural Network(ANN)is proposed to combine the advantages of AFM and SEM for the accurate and efficient measurements of geometrical parameters.To improve measurement efficiency,an automated measurement process utilizing deep learning has also been proposed.AFM and SEM measurement models are established to simulate training data for the ANN.This network can predict geometrical parameters more accurately with high efficiency,which can be achieved through individual techniques.Finally,the effectiveness of this method is validated by exemplary measurements for the determination of step height and pitch.This proposed approach also provides a promising solution for the laboratory-to-fab transition of metrology for semiconductors,for which automation and hybrid metrology are necessary.
基金supported by Guangdong Basic and Applied Basic Research Foundation(2022A1515011200)Science and Technology Planning Project of Guangdong Province of China(STKJ2021129)State Key Laboratory for Geo-Mechanics and Deep Underground Engineering of China University of Mining&Technology(SKLGDUEK2005).
摘要The mechanical properties of clayey soils are intimately linked to Bound Water Content(BWC).Microbially Induced Calcium Precipitation(MICP),as an emerging technology for slope treatment and foundation engineering,has an unclear mechanism in the influence on BWC of soil.Therefore,quantitative analysis of the Thickness of the Bound Water Film(TBWF)—a direct microscale characterization of BWC—holds significant importance.To quantitatively analyze the influence of MICP technology on TBWF,this study proposes a TBWF prediction model based on soil mechanics theory and validates effectiveness through Atomic Force Microscopy(AFM)experiments.Taking Granite Residual Soil(GRS)as the research object,the study revealed that MICP technology significantly reduces TBWF:when the cementation solution concentration was 1.0 mol/L,TBWF decreased from 48.297 nm(untreated control)to 34.561 nm(1.0 mol/L treatment group),a reduction of 28.44%.Further investigations revealed that MICP treatment lowers the liquid limit moisture content of soil while increasing specific gravity,bound water density,and specific surface area.However,when the concentration exceeded 1.0 mol/L,TBWF rebounded due to suppressed urease activity.AFM experimental data showed high consistency with theoretical model predictions,verifying the model’s reliability.This study provides microscopic mechanism support for the application of MICP technology in geotechnical engineering fields such as landslide prevention and slope reinforcement,establishes a new method for quantitative analysis of the bound water film,and holds important significance for improving the effectiveness of geological disaster prevention and control.
摘要【目的】探究温拌沥青混合料黏附机理,拓展温拌沥青与集料黏附性研究方法。【方法】使用原子力显微镜(atomic force microscope,AFM)测得分别加入Sasobit类温拌剂H01(质量分数分别为1.0%、3.0%、5.0%)和表面活性剂类温拌剂H02(质量分数分别为0.3%、0.5%、0.7%)的温拌沥青试样黏附力,并经JKR(Johnson-Kendall-Roberts)模型和表面能理论将其转换为沥青表面能,计算沥青-花岗岩和沥青-玄武岩体系的无水、有水黏附功,并与水煮试验、水稳定性试验结果进行对比分析;对基于AFM测得的温拌沥青退针力曲线进行积分,得到黏附性指标G,并将其与沥青表面能进行相关性拟合。【结果】两类温拌剂的加入均降低了温拌沥青-集料体系的黏附功,温拌沥青-花岗岩体系的无水黏附功比温拌沥青-玄武岩体系的大,有水黏附功则相反;基于AFM求解得到的H01温拌沥青-玄武岩体系黏附功的变化趋势与水稳定性试验结果相同,而H02温拌沥青-玄武岩体系的则相反;黏附性指标G与沥青表面能之间具有较好的相关性。【结论】相比温拌沥青-玄武岩体系,温拌沥青-花岗岩体系更容易发生水损害;基于AFM求解沥青-玄武岩体系黏附功的方法适用于Sasobit类温拌沥青,不适用于表面活性剂类温拌沥青;本文提出的黏附性指标G为评价温拌沥青-集料体系黏附性提供了新的量化指标。
基金supports from the National Natural Science Foundation of China(Grant No.52474059,Grant No.52174046)are greatly acknowledged.
摘要Fine particle detachment and subsequent migration can lead to severe pore plugging and consequent permeability decline.Therefore,it is crucial to quantify the critical condition when fine particle detachment occurs.The frequently observed deviations or even contradictions between experimental results and theoretical predictions of fines detachment arise from an insufficient understanding of adhesion force that can be highly influenced by salinity and temperature.To clarify the intrinsic influence of salinity and temperature on fines detachment,adhesion forces between carboxyl microspheres and hydrophilic silica substrates in an aqueous medium were measured at various salinities and tempera-tures using atomic force microscopy(AFM).The AFM-measured adhesion force decreases with increasing salinity or temperature.Trends of mean measured adhesion forces with temperature and salinity were compared with the DLVO and XDLVO theories.DLVO theory captured the trend with temperature via the impact of temperature on electric double layer interactions,whereas XDLVO theory captured the observed trend with salinity via the impact of salinity on the repulsive hydration force.Our results highlight the significance of hydration force in accurately predicting the fate of fines in porous media.
摘要探索了一种X射线反射(X-ray reflectance,XRR)表征六方氮化硼(Hexagonal boron nitride,h-BN)薄膜厚度的测试方法。通过金属有机化学气相沉积在蓝宝石衬底上生长不同厚度的h-BN薄膜,并利用拉曼光谱表征,证实了h-BN薄膜在蓝宝石衬底上的成功生长。对比了不同生长条件的h-BN与蓝宝石衬底的XRR测试曲线,表明XRR测试曲线振荡与薄膜厚度强相关。通过拟合XRR测试曲线来获得h-BN的厚度值与材料密度值,经对比发现拟合厚度值与原子力显微镜(Atomic force microscopy,AFM)测试或透射电子显微镜(Transmission electron mi‑croscopy,TEM)测试值一致,同时发现3.5 nm厚度的样品材料密度值与体材料密度值接近,但2 nm以下样品的材料密度均值低于体材料。