Quality Prediction for Silicon Wafer Wire Saw Process Based on Small Sample Data-Driven Model
In monocrystalline silicon processing,the time-consuming and high cost of quality detection make it difficult to detect the quality of silicon wafers.Therefore,a silicon wafer quality prediction method based on WGAN-GP data processing and SeResNet was proposed.The mechanism of multi-wire saw of silicon wafer was analyzed,the process factors affecting the quality of silicon wafer were identified,the data samples were established,and WGAN-GP was used to enhance the sample data.On this basis,a prediction model for total thickness variation of silicon wafers was established based on SeResNet convolutional neural network.Taking the monitoring data of multi-wire cutting process of silicon wafer as the model verification data,the prediction model of total thickness variation of silicon wafer was verified.The experimental results show that the model has good generalization and high accuracy,the prediction problem under the small sample data is effectively solved,and the overall thickness deviation prediction with the average relative error less than 10％is re-alized.So it is of great significance to replace the quality detection in silicon wafer processing with data-driven based on silicon wafer quality prediction.
silicon waferwire sawingprediction of total thickness variationgenerative adversarial networkdata enhancement
万方数据
维普
