| dc.description.abstract | In recent years, the third generation of semiconductors has developed rapidly. With its high temperature resistance and high power characteristics, it has good effect in the fields of electric vehicles and power supplies and it can operate in extreme environments ,simplify the circuit design ,design of electronic products and Cooling system, etc. However, SiC, one of the representative materials of the third generation semiconductor, is relatively expensive. Since SiC material requires a harsh environment for crystal growth, and the crystal growth rate of SiC is slow, it requires several number of processing procedures before it can be put into use, resulting in SiC Wafer production is difficult. In order to reduce silicon carbide material costs, wafer manufacturing technology must be optimized.Wire Electrical Discharge Machining is a mature and high-precision processing technology. It has a variety of controllable parameters that can be adjusted according to the target conductor material. By changing some parameters, the processing quality of the workpiece can be improved, and the gap can also be monitored in real time during the processing process. dynamic parameters such as voltage. In order to optimize the processing parameters of WEDM and analyze the impact of parameter configuration on the processing quality of SiC, this study improves the processing process through deep learning. The model uses The Long Short Term Memory (LSTM) neural network for prediction. The LSTM model is good at learning time series and analyzing the causal relationship characteristics of the time series. It collects the parameter change time series during the wire cutting process as input. The model can effectively analyze the impact of dynamic parameter changes of the wire cutting on the processing quality. Finally, facing the problem that SiC samples has few samples and difficult to collect, this study uses model training with the transfer learning method. The transfer learning method can reduce the number of SiC learning samples required for model training. Training with SiC samples and a large number of similar silicon wafer samples can improve the generalization of the model, solve
problems such as overfitting, and enrich the model training set.
The purpose of this study is to establish a WEDM quality prediction model, use instruments to collect time-series data of the cutting process, analyze the impact of each processing parameter on the surface roughness and kerf width of silicon carbide wafers and establish LSTM model with two transfer learning methods. The model outputs the surface roughness and kerf width of the sample. The input of this experiment is a combination of two dynamic parameter time-series data and three static parameters. The static parameters have a total of 27 parameter configurations. There are 270 samples of silicon wafer as the source domain data set and 270 samples of SiC as the target data set and the ratio of training set to validation set is 6:4. According to the prediction results of the validation set, the R-square values of surface roughness and kerf width are about 95 and 82 respectively. It can be seen that this model can effectively predict the processing quality of silicon carbide and reduce processing Material costs on process and deep learning. | en_US |