Resonant tunneling of holes in double-barrier structures in the presence of an in-plane magnetic field

Using the asymptotic transfer-matrix method, we investigate me resonant tunneling of holes in double-barrier semiconductor structures in the presence of an in-plane magnetic field. The transmission coefficients including ll (light to light hole), hl (light to heavy hole), hh (heavy to heavy hole), and lh (heavy to light hote) are calculated as a function of energy. As in the case of nonzero parallel wave vectors, the mixing of note tunneling can also occur due to the in-plane magnetic field. Moreover, as has been observed by resonant magnetotunneling spectroscopy, we also find that the different resonances have quite different magnetic-field dependences. © 1996 American Institute of Physics.published_or_final_versio

Effects of sputtering and annealing temperatures on MOS capacitor with HfTiON gate dielectric

In this work, Al/HfTiON/n-Si capacitors with different sputtering and annealing temperatures are studied. Larger accumulation capacitance and flat-band voltage are observed for samples with higher sputtering or post-deposition annealing temperature. Gate conduction mechanisms are only affected by sputtering temperature slightly. The flat-band voltage shift and interface-state density at midgap under high-field gate injection and substrate injection are investigated, and the results imply electron detrapping in the gate dielectric. ©2009 IEEE.published_or_final_versionThe IEEE International Conference of Electron Devices and Solid-State Circuits (EDSSC 2009), Xi'an, China, 25-27 December 2009. In Proceedings of EDSSC, 2009, p. 209-21

Introducing a framework to assess newly created questions with Natural Language Processing

Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e.g., learning path customization, drop-out prediction). However, the questions have to be assessed as well and, although it is possible to estimate with IRT the characteristics of questions that have already been answered by several students, this technique cannot be used on newly generated questions. In this paper, we propose a framework to train and evaluate models for estimating the difficulty and discrimination of newly created Multiple Choice Questions by extracting meaningful features from the text of the question and of the possible choices. We implement one model using this framework and test it on a real-world dataset provided by CloudAcademy, showing that it outperforms previously proposed models, reducing by 6.7% the RMSE for difficulty estimation and by 10.8% the RMSE for discrimination estimation. We also present the results of an ablation study performed to support our features choice and to show the effects of different characteristics of the questions' text on difficulty and discrimination.Comment: Accepted at the International Conference of Artificial Intelligence in Educatio

Au/GaAs interface annealing study by positron-lifetime spectroscopy

Structural changes of annealed Au contacts on semi-insulating GaAs have been observed by conventional positron lifetime-spectroscopy in which a significant fraction of positrons are drifted from a Na22 source to the contact by an electric field. For annealing temperatures below 200 °C it is found that the interface traps positrons into microvoids with a characteristic positron lifetime of 380±10 ps. For annealing in the range of 300-400 °C a 150±20 ps additional component appears in the lifetime spectra that is attributed to positrons annihilating from AuGa phases at the interface. The most likely explanation for this sudden onset of the positron component in the AuGa phases is that the changes in the GaAs near surface electron chemical potential, brought about by annealing, affect the interfacial dipole in such a way as to allow a favorable potential barrier for positron penetration into the metallic phase. © 1995 The American Physical Society.published_or_final_versio

Defect formation and annealing behaviors of fluorine-implanted GaN layers revealed by positron annihilation spectroscopy

Defect formation and annealing behaviors of fluorine-implanted, unintentionally doped GaN layers were studied by positron annihilation spectroscopy (PAS). Single Ga vacancies (VGa) were identified as the main vacancy-type defects detected by PAS after fluorine implantation at 180 keV with a dose of 1× 10 15 cm -2. Implantation-induced VGa tend to aggregate and form vacancy clusters after postimplantation annealing in N 2 ambient at 600 °C. Fluorine ions tend to form F-vacancy complexes quickly after thermal annealing, which is consistent with the proposed diffusion model that predicts the behaviors of fluorine in GaN. © 2009 American Institute of Physics.published_or_final_versio

Incremental Mutual Information: A New Method for Characterizing the Strength and Dynamics of Connections in Neuronal Circuits

Understanding the computations performed by neuronal circuits requires characterizing the strength and dynamics of the connections between individual neurons. This characterization is typically achieved by measuring the correlation in the activity of two neurons. We have developed a new measure for studying connectivity in neuronal circuits based on information theory, the incremental mutual information (IMI). By conditioning out the temporal dependencies in the responses of individual neurons before measuring the dependency between them, IMI improves on standard correlation-based measures in several important ways: 1) it has the potential to disambiguate statistical dependencies that reflect the connection between neurons from those caused by other sources (e. g. shared inputs or intrinsic cellular or network mechanisms) provided that the dependencies have appropriate timescales, 2) for the study of early sensory systems, it does not require responses to repeated trials of identical stimulation, and 3) it does not assume that the connection between neurons is linear. We describe the theory and implementation of IMI in detail and demonstrate its utility on experimental recordings from the primate visual system

Mobile agent path planning under uncertain environment using reinforcement learning and probabilistic model checking

The major challenge in mobile agent path planning, within an uncertain environment, is effectively determining an optimal control model to discover the target location as quickly as possible and evaluating the control system's reliability. To address this challenge, we introduce a learning-verification integrated mobile agent path planning method to achieve both the effectiveness and the reliability. More specifically, we first propose a modified Q-learning algorithm (a popular reinforcement learning algorithm), called Q EA−learning algorithm, to find the best Q-table in the environment. We then determine the location transition probability matrix, and establish a probability model using the assumption that the agent selects a location with a higher Q-value. Secondly, the learnt behaviour of the mobile agent based on Q EA−learning algorithm, is formalized as a Discrete-time Markov Chain (DTMC) model. Thirdly, the required reliability requirements of the mobile agent control system are specified using Probabilistic Computation Tree Logic (PCTL). In addition, the DTMC model and the specified properties are taken as the input of the Probabilistic Model Checker PRISM for automatic verification. This is preformed to evaluate and verify the control system's reliability. Finally, a case study of a mobile agent walking in a grids map is used to illustrate the proposed learning algorithm. Here we have a special focus on the modelling approach demonstrating how PRISM can be used to analyse and evaluate the reliability of the mobile agent control system learnt via the proposed algorithm. The results show that the path identified using the proposed integrated method yields the largest expected reward.</p