Automated Analysis of AFM Data of High-Density Cu Pad for Fine Pitch Wafer-to-Wafer (W2W) and Chip-to-Wafer (C2W) Hybrid Bonding
2022 IEEE 24th Electronics Packaging Technology Conference (EPTC)(2022)
Institute of Microelectronics
Abstract
With the advancement of 3D packaging, hybrid bonding is the most widely explored technology for heterogeneous integration and stacking of dies. For the hybrid bonding, prior measurement of the surface roughness, dielectric erosion, and dishing/protrusion of the copper bond pads is critical to check the quality of the fabricated wafers. Generally, atomic force microscopy (AFM) is used to collect the surface morphology of the wafers, and then the manual measurement is done for each scanned file which is quite time-consuming. Therefore, in this article, an automated method of analysis of AFM data was developed in Python to measure critical surface parameters on the wafers used in hybrid bonding. The Python code was used to measure the surface roughness, dishing/protrusion of bond pads with different shapes, i.e., circular and square. The use of the code provides a quick, efficient, first-order analysis methodology for evaluating the quality of the bonding surface, thereby, significantly reducing the manual time required in data crunching.
MoreTranslated text
Key words
3D packaging,AFM data,AFM data analysis,atomic force microscopy,automated analysis,bonding surface,C2W,chip-to-wafer hybrid bonding,copper bond pad dishing,copper bond pad potrusion,critical surface parameters,Cu/el,data crunching,dielectric erosion,dies stacking,efficient order analysis methodology,first-order analysis methodology,heterogeneous integration,high-density copper pad,manual measurement,pitch wafer-to-wafer,pitch wafer-to-wafer hybrid bonding,Python code,quick order analysis methodology,surface morphology,surface roughness,W2W
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