Multi-tier N=4 Binary Stacking, combining Face-to-Face and Back-to-Back Hybrid Wafer-to-Wafer Bonding Technology
IEEE 71ST ELECTRONIC COMPONENTS AND TECHNOLOGY CONFERENCE (ECTC 2021)(2021)
IMEC
Abstract
A binary wafer-to-wafer stacking scheme is advantageous over a sequential approach in terms of manufacturing cost and its impact on the stacked system yield. In this paper, such a binary wafer-to-wafer stacking flow is demonstrated. Two full thickness wafers are paired Face-to-Face using a 2 mu m pitch hybrid bonding technology, followed by top wafer thinning to 5 mu m. The Face-to-Face connections are fed through to the thinned top wafer surface by means of a 1 mu m diameter by 5 mu m deep via-last TSV. 2 mu m pitch hybrid backside pads are realized on top of these via-last TSVs, at the same time levelling out and planarizing the backside of the Face-to-Face bonded wafer pairs. Two N=2 Face-to-Face bonded wafer pairs are Back-to-Back hybrid bonded to each other, realizing an N=4 multi-tier wafer stack. The N4 top wafer is thinned to 5 mu m, revealing the nails of 5 mu m diameter by 8 mu m deep v ia-middle TSVs, implemented on this N4 wafer prior to the Face-to-Face bonding. An N4 backside passivation and an aluminum METPASS module finishes the multi-tier N=4 process flow. The paper describes the realization of the above explained integration flow. Several process challenges are extensively elaborated. Electrical results, featuring 100% yielding Back-to-Back kelvin and interwoven chains connections, demonstrate the maturity of this multi-tier N=4 binary stacking process.
MoreTranslated text
Key words
3D Integration,hybrid wafer bonding,wafer level stacking,TSV,Via-last
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