Two-dimensional imaging of hotspots in superconducting bridges by low-temperature scanning electron microscopy

Hotspots generated in a thin-film superconductor through dissipation of Joule energy are imaged by scanning the sample with the electron beam of a scanning electron microscope equipped with a liquid He stage. The signal utilized for imaging is the change of the sample voltage caused by the beam irradiation. A theoretical analysis of the heat balance equation indicates that the voltage signal reaches a maximum at the hotspot boundaries where the temperature profile passes through the critical temperature value of the superconductor. The predictions of our theoretical model agree well with our experiments performed using films of pure Sn, O₂-doped Sn, and O₂-doped Al. The width of the signal peaks marking the hotspot boundaries is twice the thermal healing length of the film/substrate configuration.Supported by a grant from the Deutsche Forschungsgemeinschaft, by the Director for Energy Research, Office of Basic Energy Sciences, and by the NATO Research Grants Program.Operated for the U.S. Department of Energy by Iowa State University under contract No. W-7405-Eng-82.     

Monolayer-Mediated Deposition of Tantalum(V) Oxide Thin Film Structures from Solution Precursors

Integration of oxide thin films with semiconductor substrates is a critical technology for a variety of microelectronic memory and circuit applications. Patterned oxide thin film devices are typically formed by uniform deposition followed by postdeposition ion-beam or chemical etching in a controlled environment. This paper reports details of an ambient atmosphere technique which allows selective deposition of dielectric oxide thin layers without postdeposition etching. In this method, substrate surfaces are selectively functionalized with hydrophobic self-assembled monolayers of octadecyltrichlorosilane by microcontact printing (μ-CP). Sol-gel deposition of ceramic oxides on these functionalized substrates, followed by mild, nonabrasive polishing, yields high-quality, patterned oxide thin layers only on the unfunctionalized regions. A variety of micrometer-scale dielectric oxide devices have been fabricated by this process, with lateral resolutions as fine as 4 μm. In this paper, we describe the solution chemistry, evolution of microstructure, and electrical properties of Ta₂O₅ thin films, as well as the stress-related mechanism which enables selective de-adhesion and resultant patterning. Selectively deposited, 80-120 nm thick Ta₂O₅ thin film capacitors were crystallized on platinized silicon at 700-800°C, and had dielectric constants of 18-25 depending upon the processing conditions, with 1 V leakage current densities as low as 2 × 10⁻⁸ A/cm². The ability to selectively deposit Ta₂O₅ and other electrical ceramics (such as LiNbO₃ and PbTiO₃) on a variety of technologically important substrate materials suggests broad potential for integrated circuit and hybrid microelectronics applications.     

Efficient multiscale regularization with applications to thecomputation of optical flow

A new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. The solution of the new problem formulation is computed with an efficient multiscale algorithm. Experiments on several image sequences demonstrate the substantial computational savings that can be achieved due to the fact that the algorithm is noniterative and in fact has a per pixel computational complexity that is independent of image size. The new approach also has a number of other important advantages. Specifically, multiresolution flow field estimates are available, allowing great flexibility in dealing with the tradeoff between resolution and accuracy. Multiscale error covariance information is also available, which is of considerable use in assessing the accuracy of the estimates. In particular, these error statistics can be used as the basis for a rational procedure for determining the spatially-varying optimal reconstruction resolution. Furthermore, if there are compelling reasons to insist upon a standard smoothness constraint, the new algorithm provides an excellent initialization for the iterative algorithms associated with the smoothness constraint problem formulation. Finally, the usefulness of the approach should extend to a wide variety of ill-posed inverse problems in which variational techniques seeking a "smooth" solution are generally used.     

Weathering a political storm: A contextual perspective on a psychological research controversy.

In the spring of 1999, a storm of controversy arose at the local, state, and national levels surrounding an article (B. Rind et al, see record 1998-04232-002) on the effects of child sexual abuse published in 1998 in Psychological Bulletin. The article was vehemently denounced by various media outlets, conservative grassroots organizations, members of the general public, state legislatures, and ultimately by the United States Congress. The authors chronicle these unprecedented events and related challenges faced by the American Psychological Association (APA). The authors also describe the Association's efforts to resolve the crisis, while staunchly upholding academic freedom and scientific integrity, and review the lessons learned for the field of psychology. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Line detection in images through regularized Hough transform.

The problem of determining the location and orientation of straight lines in images is of great importance in the fields of computer vision and image processing. Traditionally the Hough transform, (a special case of the Radon transform) has been widely used to solve this problem for binary images. In this paper, we pose the problem of detecting straight lines in gray-scale images as an inverse problem. Our formulation is based on use of the inverse Radon operator, which relates the parameters determining the location and orientation of the lines in the image to the noisy input image. The advantage of this formulation is that we can then approach the problem of line detection within a regularization framework and enhance the performance of the Hough-based line detector through the incorporation of prior information in the form of regularization. We discuss the type of regularizers that are useful for this problem and derive efficient computational schemes to solve the resulting optimization problems enabling their use in large applications. Finally, we show how our new approach can be alternatively viewed as one of finding an optimal representation of the noisy image in terms of elements chosen from a dictionary of lines. This interpretation relates the problem of Hough-based line finding to the body of work on adaptive signal representation.