Silicon wafers are the primary semiconductor substrates used to fabricate Integrated Circuits (ICs). Recently, the industry is making a transition from 200 to 300 mm wafers. To attain very flat 300 mm silicon wafers, grinding has been used to flatten the wire-sawn wafers. However, it is challenging for grinding to remove the waviness induced in wire sawing. To enhance the waviness removal ability of grinding process, several approaches have been explored including soft-pad grinding. This paper presents a study on soft-pad grinding of 300 mm wire-sawn silicon wafers through Finite Element Analysis (FEA) with designed experiments. A 2 5 (five factors, two levels) full factorial design is employed to reveal the main effects as well as the interaction effects of five factors (elastic modulus, Poisson's ratio and thickness of the soft pad; waviness wavelength and waviness height of silicon wafers) on the effectiveness of waviness removal. FEA simulation results are compared with relevant experimental results. Implications of this study to manufacturing are also discussed.
Silicon is the primary semiconductor material used to fabricate microchips. The quality of microchips depends directly on the quality of the starting silicon wafers. One of the manufacturing problems in the manufacturing of silicon wafers is the presence of waviness on the surface as a result of wire-sawn slicing. To reduce this waviness, soft-pad grinding, a patented method, is used. Many factors influence the waviness reduction capacity during soft-pad grinding. The method of finite element analysis has been used to analyze the various factors. However, the grinding process is very complicated, the various factors are very vague and difficult to define. In this research, the recently developed fuzzy-neural adaptive network, which is ideally suited for the modeling of vague phenomenon, is used to model and to improve this waviness problem. To illustrate the usefulness of the approach, the process is modeled based on simulation data. The results, even though based on some very limited data, illustrate the influences of the various factors clearly.
Cutting mechanism and characteristics of difficult-to-cut materials have a great difference. Currently, systems or indicators of the machinability are so many that we do not know how to utilize them. Not only because there is no consideration the impact of cutting conditions and parameters on machinability, but they are not be accurate quantitative responses to the machinability of workpiece materials, especially to the difficult-to-cut materials. Based on in-depth analysis of cutting mechanism and objective functions of cutting parameters optimization, an innovative machinability evaluation system based on the variable processing cost per unit material is developed. And the economics of machining process are introduced to the machinability evaluation system. Several classic analysises and calculation examples of the machinability of several typical difficult-to-cut materials have been presented at the end of paper to give a clear picture from the application of the system. The result demonstrates that the machinability evaluation system based on the variable processing cost per unit material has good practicability and maneuverability.
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