In recent years, the photoelectric technology has been successfully and widely applied to wireless communication, fiber-optical, solar power, display and illumination industry. The function of photoelectric products is relied on the device performance of compound semiconductor epitaxy wafer with quality and yield that are dominated by substrate and epitaxy technology mainly. The destroyed test with high cost and sampling risk is implemented to assure the current gain of epitaxy wafer.This study proposes a prediction model for current gain based on artificial neural networks (ANNs). Response surface methodology was employed to optimize the parameters of ANN prediction model. In order to enhance the prediction accuracy, the training dataset was partitioned off into three subsets and trained by three separate ANN models. Real data collected from manufacturing process are provided in this study to demonstrate the superiority of the proposed ANN prediction model. The results indicate that the proposed ANN prediction model performs better than traditional regression analysis method. Neural networks trained by partitioned data subsets can predict better than the neural network trained by the whole dataset.