| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Late News Poster Session
|
| Presentation Title |
L-53: Computer Vision for Microvia Characterization |
| Author(s) |
Nikhil Damani, Pragna Bhaskar, Mohan Kathaperumal, Madhavan Swaminathan |
| On-Site Speaker (Planned) |
Nikhil Damani |
| Abstract Scope |
Microvia technology is essential to building high density interconnects for higher functionality/area and better performance for advanced computing and artificial intelligence applications. These vias are fabricated on the dielectric material by laser drilling. The laser drilling process is affected by material properties and the laser parameters. Fabrication of microvias involves optimizing these laser drilling parameters and follow-up processes to obtain clean, through-hole vias for each dielectric material. To ensure repeatability, multiple vias are drilled and the dimensions of all these need to be measured. Previous studies have used a combination of scanning electron microscopy and laser confocal microscopy techniques to image and measure these microvias. However, these are time consuming, destructive, and can be used only for characterizing a small number of vias. The present study uses a computer-vision (CV) based method to characterize the large number of vias drilled during the optimization of drilling process. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Characterization, |