Determination of cutting parameters for silicon wafer with a Diamond Wire Saw using an artificial neural network

Abstract An Artificial Neural Network (ANN) simulation was utilized to predict surface roughness values ( R a ) for a Silicon (Si) ingot cutting operation with a Diamond Wire Saw (DWS) cutting machine. Experiments were done on a DWS cutting machine to obtain data for training, testing and validation of the ANN. The DWS cutting operation had three parameters affecting surface quality: spool speed, z axis speed and oil ratio in a coolant slurry. Other parameters such as wire tension, wire thickness, and work piece diameter were assumed as constant. The DWS cutting machine performed 28 cutting operations with different values of the selected three parameters and new cutting parameters were derived for different cutting conditions to achieve the best surface quality by using the ANN. Wafers 400 µm thick were cut from a n -type single crystalline Si ingot in a STX 1202 DWS cutting machine. R a values were measured three times from different regions of the wafers. In ANN simulation 70% of R a values were used as training, 15% of R a values were used as validation and 15% of R a values were used to test data in ANN. The ANN simulation results validated training output data with success above 99%. Consequently, the R a values corresponding to the cutting parameters, and also proper cutting parameters for specific R a values were determined for DWS cutting using the ANN.