Search for: All records

Recent technology development of logic devices based on 2-D semiconductors such as MoS2, WS2, and WSe2 has triggered great excitement, paving the way to practical applications. Making low-resistance p-type contacts to 2-D semiconductors remains a critical challenge. The key to addressing this challenge is to find high-work function metallic materials which also introduce minimal metal-induced gap states (MIGSs) at the metal/semiconductor interface. In this work, we perform a systematic computational screening of novel metallic materials and their heterojunctions with monolayer WSe2 based on ab initio density functional theory and quantum device simulations. Two contact strategies, van der Waals (vdW) metallic contact and bulk semimetallic contact, are identified as promising solutions to achieving Schottky-barrier-free and low-contact-resistance p-type contacts for WSe2 p-type field-effect transistor (pFETs). Good candidates of p-type contact materials are found based on our screening criteria, including 1H-NbS2, 1H-TaS2, and 1T-TiS2 in the vdW metal category, as well as Co3Sn2S2 and TaP in the bulk semimetal category. Simulations of these new p-type contact materials suggest reduced MIGS, less Fermi-level pinning effect, negligible Schottky barrier height and small contact resistance (down to 20 Ωμm ) 

Abstract The increase in the resistivity with decreasing temperature followed by a drop by more than one order of magnitude is observed on the metallic side near the zero-magnetic-field metal-insulator transition in a strongly interacting two-dimensional electron system in ultra-clean SiGe/Si/SiGe quantum wells. We find that the temperature $$T_{\text {max}}$$ T max , at which the resistivity exhibits a maximum, is close to the renormalized Fermi temperature. However, rather than increasing along with the Fermi temperature, the value $$T_{\text {max}}$$ T max decreases appreciably for spinless electrons in spin-polarizing (parallel) magnetic fields. The observed behaviour of $$T_{\text {max}}$$ T max cannot be described by existing theories. The results indicate the spin-related origin of the effect. 

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the following two components: (1) the vector representations of local contents of images and (2) the matrix representations of local pixel displacements caused by the relative motions between the agent and the objects in the 3D scene. When the image frame undergoes changes due to local pixel displacements, the vectors are multiplied by the matrices that represent the local displacements. Thus the vector representation is equivariant as it varies according to the local displacements. Our experiments show that our model can learn Gabor-like filter pairs of quadrature phases. The profiles of the learned filters match those of simple cells in Macaque V1. Moreover, we demonstrate that the model can learn to infer local motions in either a supervised or unsupervised manner. With such a simple model, we achieve competitive results on optical flow estimation. 

There is little research or understanding of curricular differences between two- and four-year programs, career development of engineering technology (ET) students, and professional preparation for ET early career professionals [1]. Yet, ET credentials (including certificates, two-, and four-year degrees) represent over half of all engineering credentials awarded in the U.S [2]. ET professionals are important hands-on members of engineering teams who have specialized knowledge of components and engineering systems. This research study focuses on how career orientations affect engineering formation of ET students educated at two-year colleges. The theoretical framework guiding this study is Social Cognitive Career Theory (SCCT). SCCT is a theory which situates attitudes, interests, and experiences and links self-efficacy beliefs, outcome expectations, and personal goals to educational and career decisions and outcomes [3]. Student knowledge of attitudes toward and motivation to pursue STEM and engineering education can impact academic performance and indicate future career interest and participation in the STEM workforce [4]. This knowledge may be measured through career orientations or career anchors. A career anchor is a combination of self-concept characteristics which includes talents, skills, abilities, motives, needs, attitudes, and values. Career anchors can develop over time and aid in shaping personal and career identity [6]. The purpose of this quantitative research study is to identify dimensions of career orientations and anchors at various educational stages to map to ET career pathways. The research question this study aims to answer is: For students educated in two-year college ET programs, how do the different dimensions of career orientations, at various phases of professional preparation, impact experiences and development of professional profiles and pathways? The participants (n=308) in this study represent three different groups: (1) students in engineering technology related programs from a medium rural-serving technical college (n=136), (2) students in engineering technology related programs from a large urban-serving technical college (n=52), and (3) engineering students at a medium Research 1 university who have transferred from a two-year college (n=120). All participants completed Schein’s Career Anchor Inventory [5]. This instrument contains 40 six-point Likert-scale items with eight subscales which correlate to the eight different career anchors. Additional demographic questions were also included. The data analysis includes graphical displays for data visualization and exploration, descriptive statistics for summarizing trends in the sample data, and then inferential statistics for determining statistical significance. This analysis examines career anchor results across groups by institution, major, demographics, types of educational experiences, types of work experiences, and career influences. This cross-group analysis aids in the development of profiles of values, talents, abilities, and motives to support customized career development tailored specifically for ET students. These findings contribute research to a gap in ET and two-year college engineering education research. Practical implications include use of findings to create career pathways mapped to career anchors, integration of career development tools into two-year college curricula and programs, greater support for career counselors, and creation of alternate and more diverse pathways into engineering. Words: 489 References [1] National Academy of Engineering. (2016). Engineering technology education in the United States. Washington, DC: The National Academies Press. [2] The Integrated Postsecondary Education Data System, (IPEDS). (2014). Data on engineering technology degrees. [3] Lent, R.W., & Brown, S.B. (1996). Social cognitive approach to career development: An overivew. Career Development Quarterly, 44, 310-321. [4] Unfried, A., Faber, M., Stanhope, D.S., Wiebe, E. (2015). The development and validation of a measure of student attitudes toward science, technology, engineeirng, and math (S-STEM). Journal of Psychoeducational Assessment, 33(7), 622-639. [5] Schein, E. (1996). Career anchors revisited: Implications for career development in the 21st century. Academy of Management Executive, 10(4), 80-88. [6] Schein, E.H., & Van Maanen, J. (2013). Career Anchors, 4th ed. San Francisco: Wiley. 

How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most effective representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose. We demonstrate that the camera movement can further be parametrized by a matrix Lie algebra that underlies a rotation system in the neural space. The vector representations are then concatenated and generate the posed 2D image through a decoder network. The model is learned from only posed 2D images and corresponding camera poses, without access to depths or shapes. We conduct extensive experiments on synthetic and real datasets. The results show that compared with other camera pose representations, our learned representation is more robust to noise in novel view synthesis and more effective in camera pose regression.