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In this paper, the General Regression Neural Network proposed to build a predicted model of the wafer probe yield that the key variables form the Wafer Acceptance Test (WAT). Using the actual situation of Taiwan DRAM production processes as an example. From the analysis results, it can get the 82 significant variables of the WAT, and the R 2 of GRNN prediction model is 0.73. After discussing with the manufacturer, the model was introduced into the actual process, and the results can effectively increase the yield and reduce quality cost in the DRAM manufacturing.

Wafer Probe Yield Prediction Modeling based on General Regression Neural Network to Improve DRAM Processes