A Class-D CMOS DCO with an on-chip LDO
European Solid State Circuits Conference（2014）
Lund Univ.
Abstract
This paper presents the co-design of a class-D digitally-controlled oscillator (DCO) and a low-dropout voltage regulator (LDO) generating the supply voltage for the DCO. Despite the high intrinsic supply pushing of the class-D oscillator topology, the LDO noise has only a very marginal impact on the DCO phase noise. The class-D DCO and LDO have been integrated in a 65 nm CMOS process without any thick top metal layer. The oscillation frequency is tunable between 3.0 GHz and 4.3 GHz, for a tuning range of 36%, with a fine frequency step below 3kHz and a fine frequency range of 10 MHz (both measured at 3GHz). Drawing 9.0 mA from 0.4V (corresponding to an unregulated supply voltage of 0.6 V), the phase noise is -145.5 dBc/Hz at a 10 MHz offset from a 3.0GHz carrier. The resulting FoM is 189.5 dBc/Hz, and varies less than 1dB across the tuning range. The FoM increases to above 190 dBc/Hz when the regulated supply voltage is 0.5 V.
MoreTranslated text
Key words
CMOS digital integrated circuits,MMIC oscillators,field effect MMIC,phase noise,voltage regulators,CMOS process,DCO phase noise,class-D CMOS DCO,class-D digitally-controlled oscillator,class-D oscillator topology,current 9.0 mA,frequency 3.0 GHz to 4.3 GHz,low-dropout voltage regulator,on-chip LDO noise,oscillation frequency,size 65 nm,supply voltage,voltage 0.4 V,voltage 0.5 V,voltage 0.6 V,CMOS,DCO,LDO,VCO,class-D,high efficiency,low phase noise,low-voltage,voltage regulator
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined