SPM-based techniques

For direct patterning on the nanometer scale, scanning probe microscopy (SPM) based techniques such as dip-pen-nanolithography (DPN), [112-114] nanograftingf, nanoshaving or scanning tunneling microscopy (STM) based techniques such as electron induced diffusion or evaporation have recently been developed (Fig. 9.14) [115, 116]. The SPM based methods, allows the deposition of as-sembhes into restricted areas with 15 nm linewidths and 5 nm spatial resolution. Current capabihties and future applications of DPN are discussed in Ref. [117]. 

The anodic oxide pattern of 100 nm dimension grown by SPM-based techniques can be used as a mask that can be transferred to a substrate below. An anodized alumina mask (produced by contact mode AFM on sputtered A1 film) has been used to transfer pattern on Si by using a combination of wet and reactive ion etching. Anodic oxidation creates a protruding oxide pattern that can be etched. The pattern can form a positive or negative mask depending on what is etched, the aluminum or the oxides. In Figure 21.14 we show an example of a nanopattern (an array of points) created by anodic oxidation on a surface of aluminum. 

Enzyme nanolithography has been introduced in 2003 and has gradually advanced since, using several SPM-based and non-SPM-based techniques. 

Among the many microscopy-based techniques for the study of biomolecules immobilised on surfaces, scanning probe microscopies (SPM) and especially atomic force microscopies (AFM) are arguably the most used techniques because of their molecular and sub-molecular level resolution and in situ imaging capability. Moreover, the invasiveness of AFM, which is less of a problem for the DNA molecules, is essential for another two functions, apart from the mapping of surface nanotopographies, namely the quantification and visualisation of the distribution of chemistry, hydrophobicity and local mechanical properties on surfaces and the fabrication of nanostructures. 

SPMs can now be found in commercial markets and specialty clothing due to their lightweight structure, liquid and aerosol repellent properties, and facilitation of moisture vapor transport. However, for military use, SPMs have limitations (Wilusz 2007). SPMs may act as liquid-repellents but may allow vapors to pass and therefore need an activated carbon layer to add extra protection capabilities. Moreover, military garments experience tremendous stress on a day-to-day basis. SPM-based ensembles are more susceptible to tearing as compared to activated carbon-based textile fabrics (Wilusz 2007). Optimizing the permselectivity of the membrane by surface modification or other such techniques is necessary to achieve a balance between comfort (e.g., moisture vapor transmission) and chemical vapor barrier properties. Furthermore, SPMs or membrane-carbon ensembles must possess acceptable mechanical strength to sustain daily military operations. 

Structural and chemical analysis. It is therefore conceivable that the realm of SPM-based nanopatterning techniques will go beyond local surface modification and become performing tools for combinatorial synthetic and analytical chemistry as well as biomolecular and medical diagnosis. ... 

Scanning probe microscopies (SPM) such as STM and AFM are powerful tools for analyzing solid surfaces. A combination of these microscopic methods and the scattering techniques could give us a new way of determining the fine structures of microcapsule surfaces. Recently, another SPM, scanning near field microscopy, has been developed [57]. The extreme limit of the resolving power of the optical microscope based on the Abbe diffraction theorem can be raised to... 

Autocorrelation in data affects the accuracy of the charts developed based on the iid assumption. One way to reduce the impact of autocorrelation is to estimate the value of the observation from a model and compute the error between the measured and estimated values. The errors, also called residuals, are assumed to have a Normal distribution with zero mean. Consequently regular SPM charts such as Shewhart or CUSUM charts could be used on the residuals to monitor process behavior. This method relies on the existence of a process model that can predict the observations at each sampling time. Various techniques for empirical model development are presented in Chapter 4. The most popular modeling technique for SPM has been time series models [1, 202] outlined in Section 4.4, because they have been used extensively in the statistics community, but in reality any dynamic model could be used to estimate the observations. If a good process model is available, the prediction errors (residual) e k) = y k)—y k) can be used to monitor the process status. If the model provides accurate predictions, the residuals have a Normal distribution and are independently distributed with mean zero and constant variance (equal to the prediction error variance). 

Models between groups of variables such as process measurements x xi and quality variables y xi be developed by using various regression techniques. Here, the subscripts indicate the vector dimensions (number of variables). If n samples have been collected for each group of variables, the data matrices are X xm and Y xg- The existence of a model provides the opportunity to predict process or product variables and compare the measured and predicted values. The residuals between the predicted and measured values of the variables can be used to develop various SPM techniques (residuals-based univariate SPM was discussed in Section 2.3.1) and tools for identification of variables that have contributed to the out-of-control signal. 

To include the information about process d3mamics in the models, the data matrix can be augmented with lagged values of data vectors, or model identification techniques such as subspace state-space modeling can be used (Section 4.5). Negiz and Cinar [209] have proposed the use of state variables developed with canonical variates based realization to implement SPM to multivariable continuous processes. Another approach is based on the use of Kalman filter residuals [326]. MSPM with dynamic process models is discussed in Section 5.3. The last section (Section 5.4) of the chapter gives a brief survey of other approaches proposed for MSPM. 

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