高速原子力显微镜的成像方法研究
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摘要
纳米科技已经成为二十一世纪最重要的技术之一，而纳米科技的发展，很大程度上依赖于纳米观测和纳米操作的工具与手段——扫描探针显微镜(SPM，Scanning Probe Microscope)。而其中的原子力显微镜(AFM，Atomic ForceMicroscope)，由于1）AFM不受样品导电性限制；2）可同时支持在大气与液相环境中观测样品，从而使其广泛应用于化学、材料、物理、生物等纳米相关科学领域。因此，原子力显微镜技术的发展，也极大的促进了纳米领域研究成果的发展。然而，随着纳米相关技术的不断发展，科研工作者对AFM的纳米测量与纳米操作性能提出了更高的要求，而现有的AFM由于自身固有的缺陷，各方面性能还未达到这些要求。主要表现为：1）扫描速度较慢，成像效率较低，对于一些实时性要求较高的活性样品，无法满足成像要求；2）高速扫描时，原子力显微镜系统复杂的非线性会导致样品形貌的成像误差大。3）扫描模式多，操作难度较大，对于扫描参数的调节需要较长时间的培训与操作经验。这些限制严重地阻碍了纳米技术领域的深入研究。
本论文针对上述问题，通过对AFM的成像方法展开深入研究，来进一步改善AFM在高速扫描时的成像性能。具体而言，本文主要工作包括以下四个方面：
第一，针对成像系统中的控制信号，提出了两种分别基于压电扫描器特性与样品特性的成像信号预处理技术。首先，为了减小压电扫描器动态特性对于扫描精度的影响，本论文通过模型辨识的方法获得压电扫描器的动态模型，将成像系统中的控制信号转化为压电扫描器的真实伸缩量，从而减小了高速AFM扫描时的成像误差。同时，由于样品在放置到载物台时很难保证样品完全水平放置，这会导致控制信号产生一个扫描斜面，为了方便后续章节的进一步处理，在此提出了一种方便、快捷地在线实时去除斜面的预处理方法。仿真与实验结果表明本文所设计的预处理方法对压电扫描器动态特性引起的成像误差有着明显的抑制作用，同时消除了由于样品放置不当而产生的形貌倾斜问题。
第二，设计了一种基于临近点集数据融合的改进动态成像方法。具体而言，在高速扫描时，为了抑制样品-探针之间强非线性特性对成像质量的影响，本文巧妙地利用扫描点邻域形貌相近似的先验知识，设计了一种基于临近点集数据融合的高速AFM成像方法。该成像方法结合AFM成像系统的非线性和先验统计信息，构造了一个滤波系数随控制误差而实时变化的非线性滤波器，来抑制因探针-样品间的非线性特性而带来成像误差。仿真与实验结果表明，该成像算法能明显提高AFM高速扫描时的成像精度。
第三，为了改善轻敲扫描模式下AFM系统的成像精度，提出了一种适用于轻敲AFM的基于参数自适应UKF的形貌高度估计方法。首先，分析了轻敲式AFM的成像系统模型，并对该模型进行了简化和离散化。在此基础上，将样品形貌信息作为过程噪声引入成像滤波系统。同时利用轻敲扫描模式中大量的中间动态数据，对该非线性成像系统进行参数变化的无味卡尔曼滤波。本文用协方差随时间变化的统计量，来估计当前过程的实际噪声水平，进而实现了过程噪声协方差参数的自适应环节。仿真结果表明，本文算法能够较大地提供AFM系统的成像精度。
第四，从AFM易用性的角度出发，设计提出了基于数据驱动的AFM参数自整定控制与成像方法。由于更换样品、扫描器、探针或扫描参数时，均会改变AFM成像系统的模型参数，因而需要重新调节控制器增益。为了增加系统的易用性，本文基于数据驱动方法，提出了一种能够自动调整参数的AFM控制与成像方法，从而降低了AFM的使用门槛。具体而言，首先引入CARIMA(Controlled Auto-Regressive and Moving-Average)参数模型来描述AFM系统局部的动态线性化模型，并通过基于数据驱动的辨识方法得到该模型的参数，然后采用基于GPC的优化方法在线计算PI控制器参数，从而得到了一种具有控制参数自动调整功能的AFM成像方法。论文最后分别用仿真数据和实验数据对该方法进行了验证，结果表明，当AFM改变扫描速度或者控制器PI参数选择不恰当时，该方法能够及时调整PI参数，减小控制误差，提高成像精度。
Nanotechnology has become one of the most significant technologies in thetwenty-first century. The development of nanotechnology relies heavily on the meansof nano-observation and nanomanipulation－Scanning Probe Microscope (SPM),among which anAtomic Force Microscope (AFM) is widely used in chemistry,materials, physics, biology and other nano-related scientific fields due to thefollowing two reasons:1) the usage of an AFM is not limited by the sampleconductivity;2) it supports the sample observation either in the atmosphere or liquidenvironments. Therefore, the development of the atomic force microscope technologyhas greatly promoted the progress in the field of nanotechnology research. However,with the fast development of nano-technology, researchers put forward higherrequirements on the AFM nano-measurement and nano operational performance, forwhich there presents significant difficulty for exisiting AFMs due to its inherentdefection. These difficulties are as follows:1) the scanning speed/efficiency is slow,and it is unable to meet the imaging requirements for real-time requirements of highlyactive samples;2) in high-speed scanning, complex non-linear characteristics ofatomic force microscopy will lead to large imaging errors of the sample topography;3) the operation of AFMs is very difficult, since there are vaious Scan Modes, and itusually takes long time to train operators for the adjustment of scanning parameters.These restrictions severely hamper the in-depth study in the field of nanotechnology.
To address these problems, this dissertation implements in-depth study for theAFM imaging methods to further improve the performance of AFMs in thehigh-speed scanning tasks. Specifically, its main work includes the following fouraspects:
First, for the control signal in the imaging system, two kinds of preprocessingtechniques are proposed based on the dynamics of the piezoelectric scanning tube andsample characteristics of the imaging signal. First, in order to alleviate the dynamiceffects, this dissertation proposes a model identification method to reduce the imaging errors in high-speed AFM scanning, based on which the control signal is transformedinto the actual stretching amount of the piezoelectric scanner. Meanwhile, it isdifficult to ensure that the sample is completely horizontal when the sample is placedon the loading platform, which causes the control signal to generate a scan slant. Inorder to facilitate further analysis of the subsequent chapters, the dissertationproposes a convenient online real-time removal preprocessing method to reduce theslant plane. Simulation and experimental results show that the proposedpreprocessing methodssignificantly inhibites the imaging errors due to the dynamiccharacteristics of the piezoelectric scanner and at the same time eliminates the slantoccuring in sample placing.
Second, the dissertation designs an improved dynamic imaging method based onthe integration of the near point set. In high speed scanning, to suppress the negativeeffect of the strong nonlinear dynamics on the AFM image quality, this dissertationproposes a high-speed AFM imaging method based on the fact that the scanning pointhas similarity with the neighborhood. This imaging method takes into accunt bothsystem nonlinearity and the priori knowledge, and thus a nonlinear filter isconstructed with its coefficient being a function of the control error signal, based onwhich the imaging error is suppressed. Simulation and experimental results show thatthe imaging algorithm can dramatically improve the accuracy of high-speed scanningof the AFM imaging.
Third, in order to improve the imaging accuracy of the tapping AFM, atopography estimation method is designed based on an adaptive unscented kalmanfilter (AUKF). The tapping-mode AFM imaging model is first analyzed, simplified,and the discrete-time model is derived. On this basis, the sample topography isintroduced into the imaging filter system as the process noise. While taking advantageof the large amount of dynamic data in tapping scanning mode, the nonlinearimagingsystem isfiltered with the parameter adaptive UKF. Thisdissertation uses the variationof the convariance over time to estimate the actual noise level of the current process,thus achievingself-adapting for the process noise covariance parameters.
Fourth, for the convenient operation of AFM, a data-driven based control andimaging method is designedto accomplish the self tunning of control parameters. When sample, scanner, probe or scanning parameters are changed, the modelparameter of the AFM imaging system can be changed accordingly. Thus, adjustmentof the controller parameters should also be made repetitively. This dissertationproposes a data-driven based control and imaging method, which is capable ofadjusting the control parameters automatically so as to reduce the difficulty in AFMoperation. Specifically, CARIMA(Controlled Auto-Regressive and Moving-Average)model is introduced to describe the local dynamiclinearized system model, which isthen identified using data-dirven based methods. Subseqently, theproportional-integral(PI) parameters is calculated online based on global predictivecontrol (GPC) optimization method, and thus the automatoic tunning of PI prametersis accomplished. Simulation and experimental results are provided to show thatwhenthe scanning speed is changed or the PI parameters are chosen unappropriately,the proposed method still works well toimprove the imaging accuracy.
本论文针对上述问题，通过对AFM的成像方法展开深入研究，来进一步改善AFM在高速扫描时的成像性能。具体而言，本文主要工作包括以下四个方面：
第一，针对成像系统中的控制信号，提出了两种分别基于压电扫描器特性与样品特性的成像信号预处理技术。首先，为了减小压电扫描器动态特性对于扫描精度的影响，本论文通过模型辨识的方法获得压电扫描器的动态模型，将成像系统中的控制信号转化为压电扫描器的真实伸缩量，从而减小了高速AFM扫描时的成像误差。同时，由于样品在放置到载物台时很难保证样品完全水平放置，这会导致控制信号产生一个扫描斜面，为了方便后续章节的进一步处理，在此提出了一种方便、快捷地在线实时去除斜面的预处理方法。仿真与实验结果表明本文所设计的预处理方法对压电扫描器动态特性引起的成像误差有着明显的抑制作用，同时消除了由于样品放置不当而产生的形貌倾斜问题。
第二，设计了一种基于临近点集数据融合的改进动态成像方法。具体而言，在高速扫描时，为了抑制样品-探针之间强非线性特性对成像质量的影响，本文巧妙地利用扫描点邻域形貌相近似的先验知识，设计了一种基于临近点集数据融合的高速AFM成像方法。该成像方法结合AFM成像系统的非线性和先验统计信息，构造了一个滤波系数随控制误差而实时变化的非线性滤波器，来抑制因探针-样品间的非线性特性而带来成像误差。仿真与实验结果表明，该成像算法能明显提高AFM高速扫描时的成像精度。
第三，为了改善轻敲扫描模式下AFM系统的成像精度，提出了一种适用于轻敲AFM的基于参数自适应UKF的形貌高度估计方法。首先，分析了轻敲式AFM的成像系统模型，并对该模型进行了简化和离散化。在此基础上，将样品形貌信息作为过程噪声引入成像滤波系统。同时利用轻敲扫描模式中大量的中间动态数据，对该非线性成像系统进行参数变化的无味卡尔曼滤波。本文用协方差随时间变化的统计量，来估计当前过程的实际噪声水平，进而实现了过程噪声协方差参数的自适应环节。仿真结果表明，本文算法能够较大地提供AFM系统的成像精度。
第四，从AFM易用性的角度出发，设计提出了基于数据驱动的AFM参数自整定控制与成像方法。由于更换样品、扫描器、探针或扫描参数时，均会改变AFM成像系统的模型参数，因而需要重新调节控制器增益。为了增加系统的易用性，本文基于数据驱动方法，提出了一种能够自动调整参数的AFM控制与成像方法，从而降低了AFM的使用门槛。具体而言，首先引入CARIMA(Controlled Auto-Regressive and Moving-Average)参数模型来描述AFM系统局部的动态线性化模型，并通过基于数据驱动的辨识方法得到该模型的参数，然后采用基于GPC的优化方法在线计算PI控制器参数，从而得到了一种具有控制参数自动调整功能的AFM成像方法。论文最后分别用仿真数据和实验数据对该方法进行了验证，结果表明，当AFM改变扫描速度或者控制器PI参数选择不恰当时，该方法能够及时调整PI参数，减小控制误差，提高成像精度。
Nanotechnology has become one of the most significant technologies in thetwenty-first century. The development of nanotechnology relies heavily on the meansof nano-observation and nanomanipulation－Scanning Probe Microscope (SPM),among which anAtomic Force Microscope (AFM) is widely used in chemistry,materials, physics, biology and other nano-related scientific fields due to thefollowing two reasons:1) the usage of an AFM is not limited by the sampleconductivity;2) it supports the sample observation either in the atmosphere or liquidenvironments. Therefore, the development of the atomic force microscope technologyhas greatly promoted the progress in the field of nanotechnology research. However,with the fast development of nano-technology, researchers put forward higherrequirements on the AFM nano-measurement and nano operational performance, forwhich there presents significant difficulty for exisiting AFMs due to its inherentdefection. These difficulties are as follows:1) the scanning speed/efficiency is slow,and it is unable to meet the imaging requirements for real-time requirements of highlyactive samples;2) in high-speed scanning, complex non-linear characteristics ofatomic force microscopy will lead to large imaging errors of the sample topography;3) the operation of AFMs is very difficult, since there are vaious Scan Modes, and itusually takes long time to train operators for the adjustment of scanning parameters.These restrictions severely hamper the in-depth study in the field of nanotechnology.
To address these problems, this dissertation implements in-depth study for theAFM imaging methods to further improve the performance of AFMs in thehigh-speed scanning tasks. Specifically, its main work includes the following fouraspects:
First, for the control signal in the imaging system, two kinds of preprocessingtechniques are proposed based on the dynamics of the piezoelectric scanning tube andsample characteristics of the imaging signal. First, in order to alleviate the dynamiceffects, this dissertation proposes a model identification method to reduce the imaging errors in high-speed AFM scanning, based on which the control signal is transformedinto the actual stretching amount of the piezoelectric scanner. Meanwhile, it isdifficult to ensure that the sample is completely horizontal when the sample is placedon the loading platform, which causes the control signal to generate a scan slant. Inorder to facilitate further analysis of the subsequent chapters, the dissertationproposes a convenient online real-time removal preprocessing method to reduce theslant plane. Simulation and experimental results show that the proposedpreprocessing methodssignificantly inhibites the imaging errors due to the dynamiccharacteristics of the piezoelectric scanner and at the same time eliminates the slantoccuring in sample placing.
Second, the dissertation designs an improved dynamic imaging method based onthe integration of the near point set. In high speed scanning, to suppress the negativeeffect of the strong nonlinear dynamics on the AFM image quality, this dissertationproposes a high-speed AFM imaging method based on the fact that the scanning pointhas similarity with the neighborhood. This imaging method takes into accunt bothsystem nonlinearity and the priori knowledge, and thus a nonlinear filter isconstructed with its coefficient being a function of the control error signal, based onwhich the imaging error is suppressed. Simulation and experimental results show thatthe imaging algorithm can dramatically improve the accuracy of high-speed scanningof the AFM imaging.
Third, in order to improve the imaging accuracy of the tapping AFM, atopography estimation method is designed based on an adaptive unscented kalmanfilter (AUKF). The tapping-mode AFM imaging model is first analyzed, simplified,and the discrete-time model is derived. On this basis, the sample topography isintroduced into the imaging filter system as the process noise. While taking advantageof the large amount of dynamic data in tapping scanning mode, the nonlinearimagingsystem isfiltered with the parameter adaptive UKF. Thisdissertation uses the variationof the convariance over time to estimate the actual noise level of the current process,thus achievingself-adapting for the process noise covariance parameters.
Fourth, for the convenient operation of AFM, a data-driven based control andimaging method is designedto accomplish the self tunning of control parameters. When sample, scanner, probe or scanning parameters are changed, the modelparameter of the AFM imaging system can be changed accordingly. Thus, adjustmentof the controller parameters should also be made repetitively. This dissertationproposes a data-driven based control and imaging method, which is capable ofadjusting the control parameters automatically so as to reduce the difficulty in AFMoperation. Specifically, CARIMA(Controlled Auto-Regressive and Moving-Average)model is introduced to describe the local dynamiclinearized system model, which isthen identified using data-dirven based methods. Subseqently, theproportional-integral(PI) parameters is calculated online based on global predictivecontrol (GPC) optimization method, and thus the automatoic tunning of PI prametersis accomplished. Simulation and experimental results are provided to show thatwhenthe scanning speed is changed or the PI parameters are chosen unappropriately,the proposed method still works well toimprove the imaging accuracy.
引文
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