深低温大功率电阻阵列封装结构研究
Infrared and Laser Engineering(2022)
Abstract
电阻阵列的封装需求向着集成度高、大功率、深低温方向发展.为了满足130 K以下低温工作、稳态功率100 W以上的深低温应用需求,提出了一种利用液氮进行制冷的集成封装结构,并利用有限元仿真和实测验证相结合的方法验证了装置的制冷能力.结果表明,热沉钼与陶瓷电极板的厚度均为2 mm的情况下,加热功率在0.1-192.76 W区间内,有限元仿真得到的温度与实测温度最大误差小于7.67%,引起误差的主要原因是封装结构件的体热阻及界面热阻随温度发生变化而仿真时采用恒定热阻.结构能够在加热功率小于211.90W的工况下正常工作.在设计的100 W稳定加热工况下,芯片衬底温度不高于101.9 K,热应力为5.66 MPa,满足设计要求.
More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