Comparison of carrier velocity gain in uniaxially and biaxially strained N-MOSFETs

2007 ◽  
Vol 43 (11) ◽  
pp. 647 ◽  
Author(s):  
T. Benshidoum ◽  
G. Ghibaudo ◽  
F. Boeuf
1983 ◽  
Vol 91 (1) ◽  
pp. 76-80 ◽  
Author(s):  
Carsten Wennmo ◽  
Nils Gunnar Henriksson ◽  
Bengt Hindfelt ◽  
Ilmari PyykkÖ ◽  
MÅNs Magnusson

The maximum velocity gain of smooth pursuit and optokinetic, vestibular, and optovestibular slow phases was examined in 15 patients with pontine, 10 with medullary, 10 with cerebellar, and 5 with combined cerebello — brain stem disorders. Marked dissociations were observed between smooth pursuit and optokinetic slow phases, especially in medullary disease. A cerebellar deficit enhanced slow phase velocity gain during rotation in darkness, whereas the corresponding gain during rotation in light was normal.


1999 ◽  
Vol 9 (2) ◽  
pp. 89-101
Author(s):  
L.J.G. Bouyer ◽  
D.G.D. Watt

Acute, reversible changes in human vestibular function can be produced by exposure to “Torso Rotation” (TR), a method involving the overuse of certain types of simple, self-generated movements. A single session results in multiple, short-lasting aftereffects, including perceptual illusions, VOR gain reduction,gaze and postural instability, and motion sickness. With repeated exposure, motion sickness susceptibility disappears and gaze stability improves. VOR gain continues to be reduced, however. Therefore, another gaze stabilizing system must come into play. Are visual and/or neck inputs involved in this functional compensation? Six subjects participated in this 7-day experiment. Eye and head movements were measured during 2 tests: 1) voluntary “head only” shaking between 0.3 and 3.0 Hz (lights off) and 2) voluntary “head and torso” shaking, moving the upper body en bloc (neck immobilized). Measurements were obtained before and repeatedly after TR. Velocity gain (eye velocity/head velocity) was determined for each of these tests. Each day, mean velocity gain during “head only” shaking in the dark (averaged over 1.0 to 2.0 Hz) dropped significantly after TR ( P < 0.01), with no long-term improvement ( P > 0.9). Similar results, although more noisy, were obtained for “head and torso” shaking. As a control, EOG calibration data confirmed that gaze stability in the light did improve over the 7 days of testing. This experiment demonstrates that the reduction in gaze instability following repeated exposure to TR results from an increased use of vision. It excludes the VOR, the COR, and predictive mechanisms (including efference copy) as contributors. In addition, in the 20 minutes following TR completion, gaze stability recovered less than during previous VOR testing in the dark. These results are compatible with the motion that exposure to TR leads to a change in sensorimotor strategy involving a de-emphasis of vestibular inputs.


2018 ◽  
Vol 38 (4) ◽  
pp. 0415001
Author(s):  
方文辉 Fang Wenhui ◽  
陈熙源 Chen Xiyuan ◽  
柳笛 Liu Di

2019 ◽  
Vol 10 ◽  
pp. 644-653 ◽  
Author(s):  
Ali Hosseingholi Pourasl ◽  
Sharifah Hafizah Syed Ariffin ◽  
Mohammad Taghi Ahmadi ◽  
Razali Ismail ◽  
Niayesh Gharaei

Nanomaterial-based sensors with high sensitivity, fast response and recovery time, large detection range, and high chemical stability are in immense demand for the detection of hazardous gas molecules. Graphene nanoribbons (GNRs) which have exceptional electrical, physical, and chemical properties can fulfil all of these requirements. The detection of gas molecules using gas sensors, particularly in medical diagnostics and safety applications, is receiving particularly high demand. GNRs exhibit remarkable changes in their electrical characteristics when exposed to different gases through molecular adsorption. In this paper, the adsorption effects of the target gas molecules (CO and NO) on the electrical properties of the armchair graphene nanoribbon (AGNR)-based sensor are analytically modelled. Thus, the energy dispersion relation of AGNR is developed considering the molecular adsorption effect using a tight binding (TB) method. The carrier velocity is calculated based on the density of states (DOS) and carrier concentration (n) to obtain I–V characteristics and to monitor its variation in the presence of the gas molecules. Furthermore, the I–V characteristics and energy band structure of the AGNR sensor are simulated using first principle calculations to investigate the gas adsorption effects on these properties. To ensure the accuracy of the proposed model, the I–V characteristics of the AGNR sensor that are simulated based both on the proposed model and first principles calculations are compared, and an acceptable agreement is achieved.


2020 ◽  
Vol 2 (9) ◽  
pp. 4179-4186 ◽  
Author(s):  
Pedro C. Feijoo ◽  
Francisco Pasadas ◽  
Marlene Bonmann ◽  
Muhammad Asad ◽  
Xinxin Yang ◽  
...  

A drift–diffusion model including self-heating effects in graphene transistors to investigate carrier velocity saturation for optimal high frequency performance.


2003 ◽  
Vol 90 (2) ◽  
pp. 972-982 ◽  
Author(s):  
Laurent Madelain ◽  
Richard J. Krauzlis

Previous research has demonstrated learning in the pursuit system, but it is unclear whether these effects are the result of changes in visual or motor processing. The ability to maintain smooth pursuit during the transient disappearance of a visual target provides a way to assess pursuit properties in the absence of visual inputs. To study the long-term effects of learning on nonvisual signals for pursuit, we used an operant conditioning procedure. By providing a reinforcing auditory stimulus during periods of accurate tracking, we increased the pursuit velocity gain during target blanking from 0.59 in the baseline session to 0.89 after 8 to 10 daily sessions of training. Learning also reduced the occurrence of saccades. The learned effects generalized to untrained target velocities and persisted in the presence of a textured visual background. In a yoked-control group, the reinforcer was independent of the subjects' responses, and the velocity gain remained unchanged (from 0.6 to 0.63, respectively, before and after training). In a control group that received no reinforcer, gain increased slightly after repetition of the task (from 0.63 to 0.71, respectively, before and after training). Using a model of pursuit, we show that these effects of learning can be simulated by modifying the gain of an extra-retinal signal. Our results demonstrate that learned contingencies can increase eye velocity in the absence of visual signals and support the view that pursuit is regulated by extra-retinal signals that can undergo long-term plasticity.


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