Back to Search
Start Over
Analytical prediction of subsurface damage depth of monocrystalline silicon in ultrasonic vibration assisted wire sawing.
- Source :
-
International Journal of Advanced Manufacturing Technology . Jul2024, Vol. 133 Issue 5/6, p2445-2460. 16p. - Publication Year :
- 2024
-
Abstract
- Diamond wire sawing is one of the key technologies in semiconductor chip and photovoltaic module manufacturing process. The existence of subsurface damage (SSD) after diamond wire sawing has a great impact on the fracture strength of monocrystalline silicon wafers, which leads to an increase in the waste rate of silicon wafers in subsequent processing. Therefore, it is necessary to evaluate the SSD depth in monocrystalline silicon wafers induced by ultrasonic vibration assisted wire sawing (UAWS). In this paper, the equal probability method is used to establish the wire saw surface morphology model. Then, according to the vibration dynamic equation of the wire saw under transverse ultrasonic, the trajectory equation of any abrasive particle is derived. On this basis, based on the material removal model and indentation fracture mechanics, an analytical prediction model of SSD depth of monocrystalline silicon wafers processed by UAWS was established. Finally, validity of the prediction model is verified by comparing with experimental results. The results show that the average error between the experimental value of the SSD depth and the theoretical value of the prediction model is 9.26%. In the UAWS process, the SSD depth of the wafer decreases with the increase of the wire saw speed and increases with the increase of the wire saw feed speed. Compared with the wire saw speed and wire saw feed rate, the workpiece rotation speed has less effect on SSD depth. The research results can be used to guide the optimization of process parameters. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02683768
- Volume :
- 133
- Issue :
- 5/6
- Database :
- Academic Search Index
- Journal :
- International Journal of Advanced Manufacturing Technology
- Publication Type :
- Academic Journal
- Accession number :
- 178333851
- Full Text :
- https://doi.org/10.1007/s00170-024-13846-8