Reduced-Size On-Wafer Inductors Using Slow Wave Techniques
2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)(2018)
Carleton Univ
Abstract
This paper introduces a study on miniaturizing on-chip RF inductors using the Slow Wave Transmission Line (SWTL) technique. The inductance of different SWTLs is calculated, simulated, fabricated and measured. The lines are treated as two-port networks where their S-parameters and ABCD parameters are extracted, from which their self-inductance is determined. The simulated results have shown a significant reduction in the coplanar microstrip transmission line length reaching 73% with a constant inductance, characteristic impedance, and electrical length. The idea was validated practically by implementing different SWTLs using CMOS 130nm process. The measured and simulated lines are compared and they have shown matched results. Practically the length reduction has reached 30% due to the constraints imposed by the process design rules on the design.
MoreTranslated text
Key words
constant inductance,two-port networks,S-parameters,ABCD parameters,self-inductance,CMOS process,reduced-size on-wafer inductors,slow wave transmission line technique,coplanar microstrip transmission line length reduction,process design rules,size 130.0 nm
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