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LED Sauna Light , Digital Image Correlation and Tracking (DIC/DDIT) is an optical method that employs tracking & image registration techniques for accurate 2D and 3D measurements of deformations, displacement and strain from the digital images. Contents 1 Overview 2 Differential Digital Image Tracking (DDIT) 3 Resolution of DIC/DDIT 4 References 5 External links 6 See also // Overview The accurate measurement of displacement and strains during deformation of advanced materials and devices continues to be a primary challenge to designers and experimental mechanicians. The increasing complexity of technological devices with stringent space requirements leads to imperfect boundary conditions that have to be properly accounted for. The push toward miniaturizing devices down to nanometer length scales imparts additional difficulties in measuring strains as the application of conventional extensometers and resistance foil gages are cumbersome, damaging, or even impossible. Compounding this problem is also the fact that compliance of small-scale testing machines precludes the use of the displacement of external actuators for estimating specimen strain. As a consequence, a technique with the following features is extremely desirable: i) no contact with the specimen required, ii) sufficient spatial resolution to measure locally at the region of interest, iii) the a bility to capture non-uniform full-field deformations, and iv) a direct measurement that does not require recourse to a numerical or analytical model. Optical methods are a logical solution to this litany of challenges. One approach is the interferometric strain/displacement gage (ISDG) developed by Sharpe., a laser-based technique that affords significant advantages over conventional strain measurement methods. ISDG utilizes two markers on the surface of the specimen that provide interference fringes that are analogous to Young two slit experiment, although in reflection as opposed to transmission. This technique offers superior resolution (as low as 5 ??) and local strain determination, but is limited to 1D measurements and requires some degree of experimental complexity. It also demands the use of markers, which have typically been either Vickers microhardness indents or patterned lines (applied by gas-assisted chemistries in the FIB or physical vapor deposition and photolithography). In the case of thin film mechanical testing where thicknesses are in the submicrometre range, hardness indents are out of the question and deposited lines can be up to an order of magnitude thicker than the specimen itself, which could significantly alter the apparent intrinsic properties of the material being tested. Digital image correlation (DIC) techniques have been increasing in popularity, especially in micro- and nano-scale mechanical testing applications due to its relative ease of implementation and use. Advances in digital imaging have been the enabling technology for this method and while white-light optics has been the predominate approach, DIC has recently been extended to SEM and AFM. Above and beyond the ability of image-based methods to provide a ox-seat to the events that are occurring during deformation, these techniques have been applied to the testing of many materials systems because it offers a full-field description and is relatively robust at tracking a wide range of arkers and varying surface contrast. The appeal of these image-based techniques, coupled with the lack of flexibility and prohibitive cost of commercial DIC software packages, provided the impetus for the development of a custom in-house software suite using the mathematical package MATLAB as the engine for calculations. This resulted in an open-source package that was uploaded to the public domain in an effort to provide free tools to users, but also to generate feedback for potential improvements and addition to the code. As such, a brief discussion of the primary features and methodology of this technique, along with some background on DIC and peak tracking will be presented here. DIC for strain measurement constitutes a major field of research and is followed by a healthy, vigorous, and dynamic discussion and discourse, so it is not the author intention to provide an exhaustive survey of the field. Instead, a more focused description of the tools required to make accurate measurements that provide insight on the deformation mechanisms that govern plasticity in nc-Al thin films is given. DIC was first conceived and developed at the University of South Carolina in the early 1980s and has been optimized and improved in recent years. DIC is predicated on the maximization of a correlation coefficient that is determined by examining pixel intensity array subsets on two or more corresponding images and extracting the deformation mapping function that relates the images (Figure 1). An iterative approach is used to minimize the 2D correlation coefficient by using nonlinear optimization techniques. The cross correlation coefficient rij is defined as Here F(xi ,yj) is the pixel intensity or the gray scale value at a point (xi ,yj) in the undeformed image. G(xi* ,yj*) is the gray scale value at a point (xi* ,yj*) in the deformed image. and are mean values of the intensity matrices F and G, respectively. The coordinates or grid points (xi ,yj) and (xi* ,yj*) are related by the deformation that occurs between the two images. If the motion is perpendicular to the optical axis of the camera, then the relation between (xi ,yj) and (xi* ,yj*) can be approximated by a 2D affine transformation such as: Here u and v are translations of the center of the sub-image in the X and Y directions, respectively. The distances from the center of the sub-image to the point (x, y) are denoted by ?x and ?y. Thus, the correlation coefficient rij is a function of displacement components (u, v) and displacement gradients . Figure 1: Basic concept of DIC DIC has proven to be very effective at mapping deformation in macroscopic mechanical testing, where the application of specular markers (e.g. paint, toner powder) or surface finishes from machining and polishing provide the needed contrast to correlate images well. However, these methods for applying surface contrast do not extend to the application of freestanding thin films for several reasons. First, vapor deposition at normal temperatures on semiconductor grade substrates results in mirror-finish quality films with rms roughnesses that are typically on the order of several nanometers. No subsequent polishing or finishing steps are required, and unless electron imaging techniques are employed that can resolve microstructural features, the films do not possess enough useful surface contrast to adequately correlate images. Typically this challenge can be circumvented by applying paint that results in a random speckle pattern on the surface, although the large and turbulent f orces resulting from either spraying or applying paint to the surface of a freestanding thin film are too high and would break the specimens. In addition, the sizes of individual paint particles are on the order of ?ms, while the film thickness is only several hundred nms, which would be analogous to supporting a large boulder on a thin sheet of paper. Very recently, advances in pattern application and deposition at reduced length scales have exploited small-scale synthesis methods including nano-scale chemical surface restructuring and photolithography of computer-generated random specular patterns to produce suitable surface contrast for DIC. The application of very fine powder particles that electrostatically adhere to the surface of the specimen and can be digitally tracked is one approach. 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