Ge4+取代对BaSnSi3O9陶瓷烧结行为和微波介电性能的优化

DU Kang,YIN Changzhi,YANG Jiaqing,ZHANG Meng,LYU Wenzhong,LEI Wen

Journal of the Chinese Ceramic Society（2023）

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Abstract

微波介质陶瓷是制造5G通信元件的关键材料,采用传统的固相反应法制备BaSn(Si1–xGex)3O9(0≤x≤1.0)微波介质陶瓷,研究Ge4+取代Si4+对BaSnSi3O9陶瓷烧结行为、晶体结构和微波介电性能的影响.结果表明:BaSnSi3O9陶瓷在最佳的1450℃烧结温度下表现出多孔的微观结构,并呈现较差的微波介电性能(介电常数εr=6.61,品质因数Q×f=7977 GHz(谐振频率为15.03 GHz),τf=?37.8×10–6/℃).通过Ge4+对Si4+的取代能形成BaSn(Si1–xGex)3O9固溶体,其晶体结构为六方结构和P-6c2空间群.采用Ge4+对Si4+的取代促进了BaSn(Si1–xGex)3O9(0≤x≤1.0)陶瓷的烧结,改变了晶体结构参数实现对陶瓷微波介电性能的优化.BaSn(Si1–xGex)3O9(0≤x≤1.0)陶瓷的Q×f值主要与Si/Ge—O和Sn—O键中共价键的比例有关,在x=1.0时BaSn(Si1–xGex)3O9陶瓷具有最优的微波介电性能:εr=8.53,Q×f=15829 GHz(谐振频率为14.41 GHz),τf=?34.2×10–6℃–1.

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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

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