Investigation of Silicon Wafer Dicing Process for High-Precision Semiconductor Manufacturing – A Review

Author

Bhasker Balpande, Dr. P .K.Sharma, Mansoor Ali


Abstract

 The rapid advancement of semiconductor technology has significantly increased the demand for high-precision wafer singulation processes capable of producing defect-free semiconductor dies with superior dimensional accuracy and high manufacturing yield. Silicon wafer dicing is one of the most critical stages in semiconductor fabrication because the quality of the diced wafer directly influences chip reliability, packaging performance, and overall device functionality. However, conventional blade dicing processes often generate defects such as edge chipping, kerf variation, micro-crack formation, surface roughness, thermal damage, and residual stresses due to the brittle nature of monocrystalline silicon. Therefore, optimization of the wafer dicing process is essential to improve machining quality while minimizing production losses.

The present research investigates the silicon wafer dicing process through an integrated experimental and numerical approach to evaluate the influence of machining parameters on cutting performance and surface integrity. Experimental investigations were performed using a precision CNC wafer dicing machine equipped with a resin-bond diamond blade under different combinations of spindle speed, feed rate, cutting depth, blade thickness, and deionized (DI) water coolant flow. The quality characteristics considered in this study include kerf width, edge chipping, surface roughness, cutting force, and cutting temperature. A Taguchi L9 orthogonal array was employed to design the experiments, while Signal-to-Noise (S/N) ratio analysis and Analysis of Variance (ANOVA) were used to identify the statistically significant machining parameters and determine their percentage contribution toward each response.



Keywords

Silicon Wafer Dicing, Semiconductor Manufacturing, Precision Machining, Finite Element Analysis (ANSYS), Taguchi Method, ANOVA.



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References


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