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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

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Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
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Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Sampling Theorem01:15

Sampling Theorem

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Bandpass Sampling01:17

Bandpass Sampling

283
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Implementation of a Reference Interferometer for Nanodetection
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A Reference-Sampling Based Calibration-Free Fractional-N PLL with a PI-Linked Sampling Clock Generator.

Jae-Soub Han1, Tae-Hyeok Eom2, Seong-Wook Choi1

  • 1School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea.

Sensors (Basel, Switzerland)
|October 26, 2021
PubMed
Summary

This study introduces a novel fractional-N Phase-Locked Loop (PLL) using reference-sampling, eliminating the need for a frequency divider (FDIV) and achieving low jitter. The design enables precise fractional-N operation without calibration, enhancing performance.

Keywords:
calibration-freefractional-Nfrequency synthesizerlow powerlow-phase noisephase locked loopreference-sampling PLLsampling clock generatorsub-sampling PLL

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Area of Science:

  • Electrical Engineering
  • Integrated Circuit Design
  • Signal Processing

Background:

  • Sampling-based Phase-Locked Loops (PLLs) offer noise reduction by removing frequency dividers (FDIVs).
  • Reference-sampling provides a wider locking range compared to sub-sampling.
  • Implementing fractional-N operation without FDIVs presents a design challenge.

Purpose of the Study:

  • To propose a novel reference-sampling-based calibration-free fractional-N PLL (RSFPLL).
  • To enable fractional-N operation without a traditional frequency divider.
  • To address design challenges in sampling-based PLLs.

Main Methods:

  • Utilized a phase-interpolator-linked sampling clock generator (PSCG) for fractional-N operation.
  • Employed phase-interpolator (PI)-based multi-phase generation instead of a frequency divider or digital-to-time converter (DTC).
  • Implemented a flexible mask window generation method to reduce power consumption.

Main Results:

  • Achieved 322 fs root-mean-square (rms) jitter.
  • Attained a figure-of-merit (FoM) of -240.7 dB.
  • Suppressed fractional spurs to -44.06 dBc.
  • Consumed 8.17 mW power.

Conclusions:

  • The proposed RSFPLL successfully integrates fractional-N capabilities into a reference-sampling PLL architecture.
  • The design demonstrates a viable method for achieving high performance and low power consumption in PLLs.
  • This work advances the field of sampling-based PLLs for improved noise and spur performance.