An SSO Based Methodology for EM Emission Estimation from SoCs
9th International Symposium on Quality Electronic Design (isqed 2008)(2008)
Texas Instrum.
Abstract
A methodology to estimate electromagnetic (EM) emission from SoCs is presented. The solution works on estimating current spectral components at the SoC periphery by performing power integrity analysis based on simultaneously switching outputs (SSO). These components are then converted to electric and magnetic dipoles. The dipoles are then analysed by a customised field solver, which computes, the field radiation patterns. Antenna models have been generated through the lead frames for quad flat and ball grid array packages. The proposed approach enables unification of SoC periphery analysis platform for timing, signal, power integrity alongwith EM emission estimation. Finally the approach has been demonstrated on various SoC periphery analysis scenarios. A memory interface of a 90 nm SOC design has been analysed and results have been compared with silicon measurements.
MoreTranslated text
Key words
SoC periphery,SoC periphery analysis platform,power integrity analysis,various SoC periphery analysis,EM emission estimation,customised field solver,field radiation pattern,power integrity,proposed approach,SOC design,EM Emission Estimation
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