RESEARCH OF WAFER-LEVEL VACUUM PACKAGING BASED ON TGV TECHNOLOGY FOR MEMS DEVICES
2020 33RD IEEE INTERNATIONAL CONFERENCE ON MICRO ELECTRO MECHANICAL SYSTEMS (MEMS 2020)
Authors: Kuang, Yunbin; Hou, Zhanqiang; Zhuo, Ming; Xu, Qiang; Li, Qingsong; Xiao, Bin; Shan, Heng; Xiao, Dingbang; Wu, Xuezhong
Abstract
This paper reports a novel wafer-level vacuum packaging method based on TGV (Through Glass Via) technology for MEMS devices. For the first time, it is reported that the vacuum packaging is achieved by TGV scheme combined with triple anodic bonding (TGV substrate, MEMS structure and glass cap), which will bring more stable vacuum packaging, lower packaging stress and lower parasitic capacitance. As a crucial step, glass reflow process was analyzed theoretically and experimentally to improve the sealing performance of TGV substrate itself. And the CMP (Chemical Mechanical Polish) technology for TGV substrate, which polishes glass and silicon at the same time, was also researched and ameliorated to obtain a smooth surface for excellent strength of triple anodic bonding. Finally, a MEMS gyroscope was packaged without getter, and the packaged pressure was calibrated to be about 90Pa, demonstrating the feasibility of this packaging scheme.
X-ray CT Image Reconstruction from Few-views via Total Generalized p-Variation Minimization
2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Authors: Zhang, Hanming; Xi, Xiaoqi; Yan, Bin; Han, Yu; Li, Lei; Chen, Jianlin; Cai, Ailong
Abstract
Total variation (TV)-based CT image reconstruction, employing the image gradient sparsity, has shown to be experimentally capable of reducing the X-ray sampling rate and removing the unwanted artifacts, yet may cause unfavorable over-smoothing and staircase effects by the piecewise constant assumption. In this paper, we present a total generalized p-variation (TGpV) regularization model to adaptively preserve the edge information while avoiding the staircase effect. The new model is solved by splitting variables with an efficient alternating minimization scheme. With the utilization of generalized p-shrinkage mappings and partial Fourier transform, all the subproblems have closed solutions. The proposed method shows excellent properties of edge preserving as well as the smoothness features by the consideration of high order derivatives. Experimental results indicate that the proposed method could avoid the mentioned effects and reconstruct more accurately than both the TV and TGV minimization algorithms when applied to a few-view problem.
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