Optimization method of cutting parameters of wafer dicing saw based on orthogonal regression design

Abstract

Abstract Wafer dicing saw is one of the core equipment in the manufacturing process of semiconductor integrated circuit components. The cutting accuracy of dicing saw directly affects the overall quality of processed chips. This paper systematically investigates the relationship between the main cutting process parameters and the cutting quality of the dicing saw. The orthogonal experimental design method and genetic algorithm are used to optimize the cutting process parameters, solving the high cost and low efficiency problems caused by the traditional way of selecting parameters by trial and error. After optimization, the average maximum chipping width is only 38.54 μm, which is 8.23% better than the traditional way of cutting quality. Based on the blade thickness of 35 μm, the maximum chipping width reached the industry-recognized best standard of 1.1 times the blade thickness, further proving the effectiveness of the method. Article Highlights The first joint application of orthogonal regression design method and evoluti-onary algorithm for parameter optimiza-tion of dicing saw. A set of optimal cutting parameters are found and verified by experiments. Compare and contrast the optimal cutting parameters cutting’s performance.

Keywords

• Dicing saw
• Orthogonal regression
• Genetic algorithm
• Parameter optimization