Quality Prediction in Semiconductor Manufacturing processes Using Multilayer Perceptron Feedforward Artificial Neural Network

Physical measurements and inspection tests of products quality in semiconductor manufacturing processes are carried out on specific equipment that often expensive and usually separated from production, which is time-consuming and production throughput decrease. Instead, predicting quality state using the available processes data has now become possible through mathematical models where this enables quick decision making in regards to product health state based on the prognosticated information about product quality. In this context, data-driven techniques based on Multilayer Perceptron Artificial Neural Network (MLP-ANN) have proposed to reveal the relationship between products end quality state and processes alarm events. Within this framework, data transformation and adaptation have discussed and model parameters selection was accomplished using the cross-validation technique. The results show a reasonable performance of the selected model that was effectively proven on a data-set collected over the whole semiconductor fabrication facility.