A5029758068- Industrial Vision Systems and Defect Detection
- Advancements in Photolithography Techniques
- Surface Roughness and Optical Measurements
- Image and Object Detection Techniques
- Image Processing Techniques and Applications
- Optical Polarization and Ellipsometry
- Integrated Circuits and Semiconductor Failure Analysis
- Optical Coatings and Gratings
- CCD and CMOS Imaging Sensors
- Spectroscopy and Chemometric Analyses
- Thin-Film Transistor Technologies
STMicroelectronics (France)
2022-2023
Laboratoire des Technologies de la Microélectronique
2020-2021
STMicroelectronics (Czechia)
2021
Spectroscopic ellipsometry is a very sensitive optical metrology technique commonly used in semiconductor manufacturing lines to accurately measure the thickness and refractive index of different layers present on specific dedicated targets wafers. In parallel, defectivity techniques are widely implemented production inspect significant amount dies representative full wafer detect physical patterning defects. A new approach can then simply emerge which apply at or die scale. This strategy,...
Mueller matrix ellipsometry (MME) is a powerful metrology tool for nanomanufacturing. The application of MME necessitates electromagnetic computations inverse problems determination in both the conventional optimization process and recent neutral network approach. In this study, we present an efficient, rigorous coupled-wave analysis (RCWA) simulation multilayer nanostructures to quantify reflected waves, enabling fast corresponding matrix. Wave propagations component layers are...
Spectroscopic ellipsometry is a very sensitive metrology technique to accurately measure the thickness and refractive index of different layers present on specific dedicated targets. In parallel, optical defectivity techniques are widely implemented in production lines inspect large number dies catch physical patterning defects during process flow. It becomes then interest explore new approach overlapping by using sensitivity tools full wafer scale. our case, spectroscopic ellipsometry's...
The paper will present an approach to automate and accelerate detection of defective wafers in high-volume manufacturing by innovatively complementing fast, non-contact non-destructive photoluminescence wafer mapping with smart image classification using deep learning methods. Specific focus is on distinguishing regularly occurring defect patterns the measurement from those revealing atypical buried, electrically active defects, which are hard detect visual inspection map. latter should be...
BackgroundSimilar to many other industries, semiconductor manufacturing is undergoing a digital transformation. The wafer fabs have been highly automated for few years, and data are everywhere, in high volume, heterogeneous, not always structured. Data analytics becoming key competence be embedded the daily lives of engineers. Among wide variety data, this paper focuses on images their classification by convolutional neural networks (CNN), which illustrated various use cases...
Full wafer measurement techniques are used in the semiconductor industry to acquire information at a large scale control process variation or detect potential defects. This usually results generation of full images, containing various objects that need be identified evaluate their impact on final product performance. Artificial intelligence is very powerful automate this identification routine. In paper, we present application Region-based Convolutional Neural Networks (RCNN) for enhanced...
On imager devices, color resists are used as optical filters to produce RGB pixel arrays. These layers deposited through spin coating process towards the end of fabrication flow, where complex topography can induce thickness inhomogeneity effect over wafer surface causing a radial striations signature, predominant at edge wafer. This deviation important yield loss but is hardly detectable with standard inline metrology or defectivity approach. In this study, an interferometry-based system...