Cross-Bridge Kelvin resistor structures for reliable measurement of low contact resistances and contact interface characterization

The parasitic factors that strongly influence the measurement accuracy of Cross-Bridge Kelvin Resistor (CBKR) structures for low specific contact resistances (rhoc) have been extensively discussed during last few decades and the minimum of the rhoc value, which could be accurately extracted, was estimated. We fabricated a set of various metal-to-metal CBKR structures with different geometries, i.e., shapes and dimensions, to confirm this limit experimentally and to create a method for contact metal-to-metal interface characterization. As a result, a model was developed to account for the actual current flow and a method for reliable rhoc extraction was created. This method allowed to characterize metal-to-metal contact interface. It was found that in the case of ideal metal-to-metal contacts, the measured CBKR contact resistance was determined by the dimensions of the two-metal stack in the area of contact and sheet resistances of the metals used

Systematic TLM Measurements of NiSi and PtSi Specific Contact Resistance to n- and p-Type Si in a Broad Doping Range

We present the data on specific silicide-to-silicon contact resistance (ρc) obtained using optimized transmission-line model structures, processed for a broad range of various n- and p-type Si doping levels, with NiSi and PtSi as the silicides. These structures, despite being attractive candidates for embedding in the CMOS processes, have not been used for NiSi, which is the material of choice in modern technologies. In addition, no database for NiSi–silicon contact resistance exists, particularly for a broad range of doping levels. This letter provides such a database, using PtSi extensively studied earlier as a reference

Cross-bidge Kelvin resistor (CBKR) structures for measurement of low contact resistances

A convenient test structure for measurement of the specific contact resistance (ρc) of metal-semiconductor junctions is the CBKR structure. During last few decades the parasitic factors which may strongly affect the measurements accuracy for ρc < 10-6 Ω • cm2 have been sufficiently discussed and the minimum of the ρc to be measured using CBKR structures was estimated. We fabricated a set of CBKR structures with different geometries to confirm this limit experimentally. These structures were manufactured for metal-to-metal contacts. It was found that the extracted CBKR values were determined by dimensions of the two-metal stack in the contact area and sheet resistances of the metals used. \ud Index Terms—Contact resistance, cross-bridge Kelvin resistor (CBKR), sheet resistance, test structures, metal, silico

REBAEV : uma abordagem para recomendação de questões utilizando o caminho do bom aprendiz e evidências de aprendizado

Orientador: Andrey Ricardo PimentelDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 09/03/2022Inclui referências: p. 84-88Área de concentração: Ciência da ComputaçãoResumo: Esta dissertação tem como objetivo descrever a abordagem ReBaEv, que consiste da recomendação da próxima questão, contextualizada no ensino da programação de computadores em cursos de computação. Esta abordagem adota duas estratégias de recomendação, o caminho do bom aprendiz e as evidências de aprendizado. O estado da arte foi identificado por meio de um mapeamento sistemático da literatura e revelou as técnicas empregadas em recomendação de questões. Considera-se o modelo do estudante, representado por um grafo genético, que é utilizado para investigar e monitorar a evolução dos aprendizes, por meio das suas habilidades. Objetiva-se utilizar o caminho do bom aprendiz, que é gerado por alunos que obtiveram êxito na realização dos exercícios, como forma de replicar um modelo competente a outros aprendizes, ao considerar o campo do aprendizado. Faz-se uso de um método de identificação automática de evidências de aprendizado, através de códigos fonte de programação, de modo a extrair a quantidade de evidências utilizadas nos exercícios. As evidências de aprendizado são empregadas na recomendação ao calcular um peso que é atribuído para os exercícios. Experimentos realizados com o objetivo de validar a abordagem mostram que as estratégias empregadas, tomando-se como base o caminho do bom aprendiz e as evidências de aprendizado, foram promissoras e favoráveis para os três cenários considerados no trabalho, quanto aos quesitos de adequação e engajamento. Por meio da avaliação com especialistas percebe-se que os exercícios recomendados, referentes aos cenários propostos, foram adequados para os diferentes tipos de alunos, e que a recomendação em uma ordem mais interessante, contribuiu com o aumento do engajamento dos alunos. Finalmente, a abordagem ReBaEv recomenda exercícios recebendo como parâmetro o estado do aluno, sendo assim, relevante com o objetivo de de aprimorar o processo de ensino-aprendizagem, ao melhor atender o aluno de acordo com as suas necessidades.Abstract: This dissertation aims to describe the ReBaEv approach, which consists of the recom- mendation of the next question, contextualized in the teaching of computer programming in computing courses. This approach adopts two recommendation strategies, the good learner path and the learning evidence. The state of the art was identified through a systematic mapping of the literature and revealed the techniques used in question recommendation. The student's model is considered, represented by a genetic graph, which is used to investigate and monitor the learners' evolution, through their abilities. The objective is to use the path of the good learner, which is generated by students who were successful in performing the exercises, as a way to replicate a competent model to other learners, when considering the field of learning. A method of automatic identification of learning evidence is used, through programming source codes, in order to extract the amount of evidence used in the exercises. The learning evidence is used in the recommendation when calculating a weight that is assigned to the exercises. Experiments carried out with the aim of validating the approach show that the strategies employed, taking as a basis the path of the good learner and the evidence of learning, were promising and favorable for the 3 scenarios considered in the work, regarding the items of adequacy and engagement . Through the evaluation with experts, it can be seen that the recommended exercises, referring to the proposed scenarios, were suitable for different types of students, and that the recommendation, in a more interesting order, contributed to increased student engagement. Finally, the ReBaEv approach recommends exercises taking the student's state as a parameter, thus being relevant in order to improve the teaching-learning process, by better serving the student according to their needs

Experimental evidence of negative linear compressibility in the MIL-53 metal–organic framework family

International audienc

Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files

First-principles computational spectroscopy is a critical tool for interpreting experiment, performing structure refinement, and developing new physical understanding. Systematically setting up input files for different simulation codes and a diverse class of materials is a challenging task with a very high barrier-to-entry, given the complexities and nuances of each individual simulation package. This task is non-trivial even for experts in the electronic structure field and nearly formidable for non-expert researchers. Lightshow solves this problem by providing a uniform abstraction for writing computational x-ray spectroscopy input files for multiple popular codes, including FEFF, VASP, OCEAN, EXCITING and XSPECTRA. Its extendable framework will also allow the community to easily add new functions and to incorporate new simulation codes.Comment: 3 pages, 1 figure, software can be found open source under the BSD-3-clause license at https://github.com/AI-multimodal/Lightsho