Post-slicing inspection of silicon wafers using the HJ-PSO algorithm under machine vision

Producing silicon wafers is a complex and important process for semiconductor manufacturers. Optimally utilizing each wafer to overcome the quartz shortage is tantamount to achieving maximum total profit. This makes the post-slicing inspection task a crucial step. This paper found that determining post-sliced wafer reuse is equal to measuring the maximum inscribed circle (MIC). This can be considered a nonlinear optimization problem. Due to silicon wafer flatness and fragility, we propose a machine vision-based inspection approach that uses a heuristic method to facilitate solving this MIC problem. The proposed method incorporates the Hooke-Jeeves pattern search with particle swarm optimization (PSO) to achieve fast convergence and quality solution. An experimental design was conducted to first verify the feasibility and identify the feasible control parameters for both PSO and the proposed HJ-PSO. Thirty defective wafers were then used to validate the proposed scheme. The experimental results reveal that the proposed scheme is effective and efficient for wafer post-slice inspection.