On the high-frequency noise figures of merit and microscopic channel noise sources in fabricated 90 nm PD SOI MOSFETs

2005 ◽  
Author(s):  
Raúl Rengel
2002 ◽  
Vol 102 (2) ◽  
pp. 329-335
Author(s):  
L. Ardaravičius ◽  
J. Liberis ◽  
A. Matulionis

1970 ◽  
Vol 13 (4) ◽  
pp. 826-838 ◽  
Author(s):  
Willard R. Thurlow ◽  
James R. Mergener

Localization of the direction of bursts of thermal noise was measured for both high-frequency and low-frequency bands, as a function of duration of bursts. Durations of 0.3, 1, 2, and 5 sec were used. Subjects were free to move their heads to aid in localization. Subjects were not specially trained in sound localization. With increase in stimulus duration, perception of elevation was slightly improved for low-frequency noise, probably due to increased information from head movement. A minimum duration of the order of 2 sec appears necessary to allow subjects to achieve maximum performance (which still is not very good for these low-frequency stimuli). Perception of the elevation of the high-frequency noise sources we used was relatively good even at the briefest duration; however, variability of judgment was larger at the shorter durations. Perception of front-back source position was much improved for both low-frequency and high-frequency noise when stimulus duration was increased. The results are understandable in terms of the increased possibility for head movement with increase in stimulus duration. It appears that one should use a minimum stimulus duration of about 2 sec if one wishes subjects to approach their most efficient performance.


2018 ◽  
Vol 64 (2) ◽  
pp. 215-224 ◽  
Author(s):  
A. I. Khil’ko ◽  
I. P. Smirnov ◽  
A. I. Mashonin ◽  
A. V. Shafranyuk

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2219
Author(s):  
Jonghwan Lee

A physics-informed neural network (PINN) model is presented to predict the nonlinear characteristics of high frequency (HF) noise performance in quasi-ballistic MOSFETs. The PINN model is formulated by combining the radial basis function-artificial neural networks (RBF-ANNs) with an improved noise equivalent circuit model, including all the noise sources. The RBF-ANNs are utilized to model the thermal channel noise, induced gate noise, correlation noise, as well as the shot noise, due to the gate and source-drain tunneling current through the potential barriers. By training a spatial distribution of the thermal channel noise and a Fano factor of the shot noise, underlying physical theories are naturally embedded into the PINN model as prior information. The PINN model shows good capability of predicting the noise performance at high frequencies.


2019 ◽  
Vol 67 (4) ◽  
pp. 315-329
Author(s):  
Rongjiang Tang ◽  
Zhe Tong ◽  
Weiguang Zheng ◽  
Shenfang Li ◽  
Li Huang

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