Signal-to-Noise Ratio, Dynamic Range, and Power Dissipation: Paying Attention to Their Interrelation Can Greatly Benefit Analog Circuit Design

2018 ◽  
Vol 10 (4) ◽  
pp. 60-69 ◽  
Author(s):  
Yannis Tsividis
Keyword(s):  
Circuit Design ◽  
Power Dissipation ◽  
Analog Circuit ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Analog Circuit Design ◽  
Signal To Noise ◽  
Noise Ratio

An Over 120dB Dynamic Range Linear Response Single Exposure CMOS Image Sensor with Two-stage Lateral Overflow Integration Trench Capacitors

2020 ◽  
Vol 2020 (7) ◽  
pp. 143-1-143-6 ◽  
Author(s):  
Yasuyuki Fujihara ◽  
Maasa Murata ◽  
Shota Nakayama ◽  
Rihito Kuroda ◽  
Shigetoshi Sugawa
Keyword(s):  
Linear Response ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Signal To Noise ◽  
Single Exposure ◽  
Two Stage ◽  
Noise Ratio ◽  
Maximum Signal

This paper presents a prototype linear response single exposure CMOS image sensor with two-stage lateral overflow integration trench capacitors (LOFITreCs) exhibiting over 120dB dynamic range with 11.4Me- full well capacity (FWC) and maximum signal-to-noise ratio (SNR) of 70dB. The measured SNR at all switching points were over 35dB thanks to the proposed two-stage LOFITreCs.

scholarly journals Estimation and Correction of Sampling Time Offsets in Tiadcs using Optimization Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1217-1222
Keyword(s):  
Optimization Algorithm ◽  
High Frequency ◽  
Performance Metrics ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Sampling Time ◽  
Signal To Noise ◽  
High Sampling ◽  
Noise Ratio ◽  
Estimation And Correction

In recent communication technologies, very high sampling rates are required for rf signals particularly for signals coming under ultra high frequency (UHF), super high frequency (SHF) and extremely high frequency (EHF) ranges. The applications include global positioning system (GPS), satellite communication, radar, radio astronomy, 5G mobile phones etc. Such high sampling rates can be accomplished with time-interleaved analog to digital converters (TIADCs). However, sampling time offsets existing in TIADCs produce non-uniform samples. This poses a drawback in the reconstruction of the signal. The current paper addresses this drawback and offers a solution for improved signal reconstruction by estimation and correction of the offsets. A modified differential evolution (MDE) algorithm, which is an optimization algorithm, is used for estimating the sampling time offsets and the estimated offsets are used for correction. The estimation algorithm is implemented on an FPGA board and correction is implemented using MATLAB. The power consumption of FPGA for implementation is 57mW. IO utilization is 27% for 4-channel TIADCs and 13% for 2-channel TIADCs. The algorithm estimated the sampling time offsets precisely. For estimation the algorithm uses a sinusoidal signal as a test signal. Correction is performed with sinusoidal and speech signals as inputs for TIADCs. Performance metrics used for evaluating the algorithm are SNR (signal to noise ratio), SNDR (signal to noise and distortion ratio), SFDR (spurious-free dynamic range) and PSNR (peak signal to noise ratio). A noteworthy improvement is observed in the above mentioned parameters. Results are compared with the existing state of the art algorithms and superiority of the proposed algorithm is verified.

A signal-to-noise ratio comparison of high dynamic range CMOS image sensors

2009 ◽  
Author(s):  
Leo H. C. Braga ◽  
Suzana Domingues ◽  
José G. Gomes ◽  
Antonio C. Mesquita
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
High Dynamic Range ◽  
Image Sensors ◽  
Signal To Noise ◽  
Cmos Image Sensors ◽  
High Dynamic ◽  
Noise Ratio

A Least-Squares Strain Estimator for Elastography

1997 ◽  
Vol 19 (3) ◽  
pp. 195-208 ◽  
Author(s):  
Faouzi Kallel ◽  
Jonathan Ophir
Keyword(s):  
Least Squares ◽  
Spatial Resolution ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Gel Phantom

A least-squares strain estimator (LSQSE) for elastography is proposed. It is shown that with such an estimator, the signal-to-noise ratio in an elastogram ( SNRe) is significantly improved. This improvement is illustrated theoretically using a modified strain filter and experimentally using a homogeneous gel phantom. It is demonstrated that the LSQSE results in an increase of the elastographic sensitivity (smallest strain that could be detected), thereby increasing the strain dynamic range. Using simulated data, it is shown that a tradeoff exists between the improvement in SNRe and the reduction of strain contrast and spatial resolution.

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