Name
Abstract | ||
|---|---|---|
The inspection of conductive particles after Anisotropic Conductive Film (ACF) bonding is a common and crucial step in the TFT-LCD manufacturing process since quality of conductive particles is an indicator of ACF bonding quality. Manual inspection under microscope is a time consuming and tedious work. There is a demand in industry for automatic conductive particle inspection system. The challenge of automatic conductive particle quality inspection is the complex background noise and diversified particle appearance, including shape, size, clustering and overlapping etc. As a result, there lacks effective automatic detection method to handle all the complex particle patterns. In this paper, we propose a U-shaped deep residual neural network (U-ResNet), which can learn features of particle from massive labeled data. The experimental results show that the proposed method achieves high accuracy and recall rate, which exceedingly outperforms the previous work. |
Year | Venue | Field |
|---|---|---|
2018 | PRCV | Anisotropic conductive film,Computer vision,Thin-film transistor,Background noise,Electrical conductor,Liquid-crystal display,Microscope,Artificial intelligence,Cluster analysis,Materials science,Particle |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kangping Chen | 1 | 0 | 0.34 |
| Eryun Liu | 2 | 0 | 0.68 |