A low-power, area-efficient all-digital delay-locked loop for DDR3 SDRAM controller

Chen, Hongming; Ma, Song; Wang, Liu; Zhang, Hao; Pan, KenYi; Yang, Cheng

doi:10.1007/s11432-014-5226-1

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…With the advantages of high stability [1][2][3] and strong portability [4][5][6][7], the all digital delay-locked loop (ADDLL) is widely used as multiphase clock generator [8][9][10][11] in double data rate (DDR) synchronous interfaces. The Open NAND Flash Interface Specification (ONFI) [12], which is the industry standard, strictly stipulates the timing requirements of non-volatile double data rate (NV-DDR) high-speed interfaces.…”

Section: Introductionmentioning

confidence: 99%

A Wide-Range Four-Phase All-Digital DLL with De-Skew Circuit

et al. 2023

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A four-phase all-digital delay-locked loop (ADDLL) with a de-skew circuit for NAND Flash high-speed interfaces is proposed. The proposed de-skew circuit adopts a fall-edge-judgment phase adjuster and a three-stage digitally controlled delay line to align the system input clock and 0∘ output clock of the four-phase DLL over a wide frequency range, thus solving the four-phase offset caused by clock skew. A parallel-cascade configuration is proposed to solve the variable phase alignment problem caused by mode switching, thus effectively improving the phase-locked accuracy. The proposed circuit is fabricated in the 0.13 μm CMOS process with a 0.072 mm2 core area. The chip testing results show an operating frequency range from 26 MHz to 1.55 GHz and a typical alignment error of approximately 17 ps.

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Section: Introductionmentioning

confidence: 99%

A Wide-Range Four-Phase All-Digital DLL with De-Skew Circuit

et al. 2023

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Vocal complexity in the long calls of Bornean orangutans

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Kazanecki

et al. 2023

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Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable 'long call' vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: 1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, 2) distinguish among call types using supervised classification and unsupervised clustering, and 3) compare the performance of different feature sets. Using 46 acoustic features, we used machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We also used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrograms. We found low inter-observer reliability and poor classification accuracy using supervised approaches, indicating that pulse types were not discrete. We propose a new pulse type classification scheme that is highly reproducible across observers and exhibits high classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and measure the gradedness of animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-á-vis graded repertoires.

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