A Visualized Quality Grading Method for Multi-index Products

A graphical system for quality assessment is highly demanded in current manufacturing industry. It is typically difficult to grade products by multi-index quality data according to specification limits, especially when there is a complex correlation between indexes. This work introduces a visualized quality grading framework to solve the problems, which includes adopting Yoe-Johnson transformation and kernel principal component analysis to augment the insufficient quality data with correlations, and then visualizing the data through a modified 3D Kiviat diagram. The confidence region of each quality grade is determined respectively on the diagram to evaluate online products. The proposed methodology is tested on a semiconductor manufacturing and the result demonstrates its effectiveness and reliability.