The National Institute of Standards and Technology Standard Reference Materials (SRM) 2460 Standard Bullets and
2461 Standard Cartridge Cases are intended for use as check standards for crime laboratories to help verify that their
computerized optical imaging equipment for ballistics image acquisitions and correlations is operating properly. Using
topography measurements and cross-correlation methods, our earlier results for the SRM bullets and recent results for
the SRM cartridge cases both demonstrate that the individual units of the SRMs are highly reproducible. Currently, we
are developing procedures for topographic imaging of the firing pin impressions, breech face impressions, and ejector
marks of the standard cartridge cases. The initial results lead us to conclude that all three areas can be measured
accurately and routinely using confocal techniques. We are also nearing conclusion of a project with crime lab experts to
test sets of both SRM cartridge cases and SRM bullets using the automated commercial systems of the National
Integrated Ballistics Information Network.
In some automated bullet identification systems, the similarity of striation marks between different bullets is
measured using the cross correlation function of the compressed signature profile extracted from a land impression.
Inclusion of invalid areas weakly striated by barrel features may lead to sub-optimal extraction of the signature profile
and subsequent deterioration of correlation results. In this paper, a method for locating striation marks and selecting valid
correlation areas based on an edge detection technique is proposed for the optimal extraction of the compressed signature
profiles. Experimental results from correlating 48 bullets fired from 12 gun barrels of 6 manufacturers have
demonstrated a higher correct matching rate than the previous study results without correlation area selection processing.
Furthermore, an attempt to convert a traditional profile with multiple z-quantization (or gray scale) levels into a binary
profile is made for the purpose of reducing storage space and increasing correlation speed.
The piezoelectric stack actuator used in Scanning Probe Microscopes (SPMs) always exhibits significant hysteresis
and creep. The hysteresis and creep will reduce the positioning precision and produce the distortion in scanning images.
Therefore it is necessary to develop a model with sufficient accuracy and stability to characterize the nonlinearities of the
piezoelectric stack actuator. In this paper, a novel hysteresis and creep model and a method for on-line identifying
parameters of this model are proposed. Experiment result shows that, actuated by triangular-wave voltage, the predicting
error using the proposed model is less than 2%, which is reduced by an order of magnitude comparing with the error
directly predicted using input voltage. The validity of this method is demonstrated by experiment result.
KEYWORDS: Line edge roughness, Line width roughness, Neural networks, Atomic force microscopy, Edge detection, Image processing, Lithium, Data processing, Edge roughness, Binary data
The line-edges of the sample scanned by AFM is detected using cellular neural networks. Line-width, Line-width roughness and line edge roughness of line-structure are calculated respectively based on the analysis of the detected edge character. Since cellular neural network is characterized by high-speed parallel computation and easy to be implemented in hardware, it has more potential, comparing with other software technique, in the quick line-structure-parameters detection.
The interaction of probe and sample is a well known factor affecting the measurement accuracy of atomic force microscopy (AFM). The emergence of ultra-sharp carbon nanotube tips provides a good approach to minimizing the distortion of the measured profile caused by interaction with the finite probe tip. However, there is nearly always a significant tilt angle resulting when the nanotube is attached to an ordinary probe. As a result, we can obtain an accurate sidewall image of only one side of the linewidth sample rather than two sides. This somewhat reduces the advantage of using nanotube probes. To solve this problem, a dual image stitching method based on image registration is proposed in this article. After the first image which provides an accurate profile of one side of the measured line is obtained, we rotate the sample 180° to obtain the second image, which provides an accurate profile of the other side of the line. We keep the sidewall data for the better side of each image and neglect the data for the other side of each image. Then, we combine these better two sides to yield a new image for which the linewidth can be calculated. The sample is inevitably located at slightly different spatial positions in the two measurements. Image registration based on an improved iterative closest point (ICP) method was applied to remove the position difference between these two images. We are working to demonstrate that the calculated sidewall angle and linewidth value after registration and stitching is more accurate than obtained from only one image.
Nano-scale linewidth measurements are performed in semiconductor manufacturing, the data storage industry, and micro-mechanical engineering. It is well known that the interaction of probe and sample affects the measurement accuracy of linewidth measurements performed with atomic force microscopy (AFM). The emergent ultra-sharp carbon nanotube tips provide a new approach to minimizing the distortion of the measured profile caused by interaction with the finite probe tip. However, there is nearly always a significant tilt angle resulting when the nanotube is attached to an ordinary probe. As a result, we can obtain an accurate sidewall image of only one side of the linewidth sample rather than two sides. This somewhat reduces the advantage of using nanotube probes. To solve this problem, a dual image stitching method based on image registration is proposed in this article. After the first image is obtained, which provides an accurate profile of one side of the measured line, we rotate the sample 180° to obtain the second image, which provides an accurate profile of the other side of the line. We keep the sidewall data for the better side of each image and neglect the data taken for the other side of each image. Then, we combine these better two sides to yield a new image for which the linewidth can be calculated.
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