Actinic detection and signal characterization of multilayer defects on EUV mask blanks

2004 ◽  
Author(s):  
Yoshihiro Tezuka ◽  
Masaaki Ito ◽  
Tsuneo Terasawa ◽  
Toshihisa Tomie
Keyword(s):  
Signal Characterization

Large-Signal Characterization of PMN-PT-Ba (90/10/3)

1997 ◽  
Author(s):  
Robert S. Janus ◽  
Mark B. Moffett ◽  
James M. Powers
Keyword(s):  
Large Signal ◽  
Signal Characterization

scholarly journals RF Small and large signal characterization of a 3D integrated GaN/RF-SOI SPST switch

2021 ◽  
pp. 1-6
Author(s):  
Frédéric Drillet ◽  
Jérôme Loraine ◽  
Hassan Saleh ◽  
Imene Lahbib ◽  
Brice Grandchamp ◽  
...  
Keyword(s):  
Power Level ◽  
Coplanar Waveguide ◽  
Average Power ◽  
Input Power ◽  
Capacitive Coupling ◽  
Rf Switch ◽  
Large Signal ◽  
Waveguide Transmission ◽  
Signal Characterization

Abstract This paper presents the radio frequency (RF) measurements of an SPST switch realized in gallium nitride (GaN)/RF-SOI technology compared to its GaN/silicon (Si) equivalent. The samples are built with an innovative 3D heterogeneous integration technique. The RF switch transistors are GaN-based and the substrate is RF-SOI. The insertion loss obtained is below 0.4 dB up to 30 GHz while being 1 dB lower than its GaN/Si equivalent. This difference comes from the vertical capacitive coupling reduction of the transistor to the substrate. This reduction is estimated to 59% based on a RC network model fitted to S-parameters measurements. In large signal, the linearity study of the substrate through coplanar waveguide transmission line characterization shows the reduction of the average power level of H2 and H3 of 30 dB up to 38 dBm of input power. The large signal characterization of the SPST shows no compression up to 38 dBm and the H2 and H3 rejection levels at 38 dBm are respectively, 68 and 75 dBc.

Accurate large-signal characterization of LDMOSFET transistor in package

2011 ◽  
Vol 53 (3) ◽  
pp. 575-579 ◽  
Author(s):  
Mohammed Tamoum ◽  
Rachid Allam ◽  
Farid Djahli
Keyword(s):  
Large Signal ◽  
Signal Characterization

Vector Signal Characterization of High-Speed Optical Components by Use of Linear Optical Sampling With Milliradian Resolution

2008 ◽  
Vol 20 (23) ◽  
pp. 2007-2009 ◽  
Author(s):  
P. A. Williams ◽  
T. Dennis ◽  
I. Coddington ◽  
W. C. Swann ◽  
N. R. Newbury
Keyword(s):  
High Speed ◽  
Optical Components ◽  
Optical Sampling ◽  
Linear Optical ◽  
Signal Characterization ◽  
Vector Signal

Characterization of the Dolphin Ultrasonic Waves Using Wavelet Transform for Autism Therapy

2018 ◽  
Vol 7 (3) ◽  
Author(s):  
FX Arinto Setyawan
Keyword(s):  
Wavelet Transform ◽  
Ultrasonic Waves ◽  
Sensory Nerves ◽  
Human Beings ◽  
Autism Therapy ◽  
Normal Human ◽  
Repetitive Activities ◽  
Signal Characterization ◽  
Special Time

Autism is a neurobiological development disorder experienced by a person from birth or toddlers. Autistic sufferers have difficulty in forming social interaction, communicating, emotional, sensory and motor disturbances as well as slow or abnormal mental development. Therefore, people with autism will be isolated from normal human beings and enter the world of repetitive, activities and obsessive interests. Autism therapy using dolphins that produce ultrasonic waves with a certain frequency can align the function of motor and sensory nerves of patients with autistic. A direct therapy using dolphins requires a special time and a cost is quite expensive for once therapy. This research proposes autism therapy with artificial dolphin ultrasonic waves based on signal characterization issued by dolphins. The main difficulty in this characterization is to eliminate the unwanted signals (noise) from the desired signal when recording the signal issued by the dolphins. Noise filtering (denoising) is done using wavelet transform. This research proposes a model of a dolphin ultrasonic wave characterization using wavelet transformation by determining decomposition and threshold levels to obtain the best signal quality for autism therapy. The effectiveness of the proposed method is measured using the SNR parameter. The results showed that the level of decomposition 5 and hard threshold gave the highest SNR value compared to the others.

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