Undeformed chip width non-uniformity modeling and surface roughness prediction in wafer self-rotational grinding process. (July 2022)
- Record Type:
- Journal Article
- Title:
- Undeformed chip width non-uniformity modeling and surface roughness prediction in wafer self-rotational grinding process. (July 2022)
- Main Title:
- Undeformed chip width non-uniformity modeling and surface roughness prediction in wafer self-rotational grinding process
- Authors:
- Tao, Hongfei
Liu, Yuanhang
Zhao, Dewen
Lu, Xinchun - Abstract:
- Abstract: Silicon wafers are commonly thinned employing an ultra-precision grinding machine based on a workpiece self-rotational principle. The diamond grains, stochastically located on the grinding wheel surface, remove the workpiece material and generate a difference in the size and shape of undeformed chips. However, an in-depth understanding of the grain-workpiece interaction randomness in wafer thinning process and its link with surface roughness has not been revealed yet. In this paper, a new index, i.e., undeformed chip width, is first proposed to analyze and describe the stochastic characteristics in material removal process as well as predict the surface roughness of ground wafer. The grinding wheel morphology is constructed by the random operation for grain size and position. Then, a 2D topography generation model is established to calculate the undeformed chip width. The non-uniform distribution characteristics of undeformed chip width under different grinding conditions are further analyzed. Finally, the relation between undeformed chip width and surface roughness of ground wafer is presented. The correctness of developed models is verified using a series of grinding experiments. Highlights: A ground wafer undeformed chip modeling framework from a micro scale is proposed. A wheel morphology is reconstructed by random operation for grain size and position. An undeformed chip width is defined to describe random properties in material removal. A regression model ofAbstract: Silicon wafers are commonly thinned employing an ultra-precision grinding machine based on a workpiece self-rotational principle. The diamond grains, stochastically located on the grinding wheel surface, remove the workpiece material and generate a difference in the size and shape of undeformed chips. However, an in-depth understanding of the grain-workpiece interaction randomness in wafer thinning process and its link with surface roughness has not been revealed yet. In this paper, a new index, i.e., undeformed chip width, is first proposed to analyze and describe the stochastic characteristics in material removal process as well as predict the surface roughness of ground wafer. The grinding wheel morphology is constructed by the random operation for grain size and position. Then, a 2D topography generation model is established to calculate the undeformed chip width. The non-uniform distribution characteristics of undeformed chip width under different grinding conditions are further analyzed. Finally, the relation between undeformed chip width and surface roughness of ground wafer is presented. The correctness of developed models is verified using a series of grinding experiments. Highlights: A ground wafer undeformed chip modeling framework from a micro scale is proposed. A wheel morphology is reconstructed by random operation for grain size and position. An undeformed chip width is defined to describe random properties in material removal. A regression model of surface roughness in wafer self-rotational grinding is derived. … (more)
- Is Part Of:
- Tribology international. Volume 171(2022)
- Journal:
- Tribology international
- Issue:
- Volume 171(2022)
- Issue Display:
- Volume 171, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 171
- Issue:
- 2022
- Issue Sort Value:
- 2022-0171-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-07
- Subjects:
- Silicon wafer -- Ultra-precision grinding -- Undeformed chip width -- Surface roughness
Tribology -- Periodicals
621.89 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00412678 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.triboint.2022.107547 ↗
- Languages:
- English
- ISSNs:
- 0301-679X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9050.217300
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21324.xml