Neurofuzzy modeling of chemical vapor deposition processes

The modeling of semiconductor manufacturing processes has been the subject of intensive research efforts for years. Physical-based (first-principle) models have been shown to be difficult to develop for processes such as plasma etching and plasma deposition, which exhibit highly nonlinear and complex multidimensional relationships between input and output process variables. As a result, many researchers have turned to empirical techniques to model many semiconductor processes. This paper presents a neurofuzzy approach as a general tool for modeling chemical vapor deposition (CVD) processes. A five-layer feedforward neural network is proposed to model the input-output relationships of a plasma-enhanced CVD deposition of a SiN film. The proposed five-layer network is constructed from a set of input-output training data using unsupervised and supervised neural learning techniques. Product space data clustering is used to perform the partitioning of the input and output spaces. Fuzzy logic rules that describe the input-output relationships are then determined using competitive learning algorithms. Finally, the fuzzy membership functions of the input and output variables are optimally adjusted using the backpropagation learning algorithm. A salient feature of the proposed neurofuzzy network is that after the training process, the internal units are transparent to the user, and the input-output relationship of the CVD process can be described linguistically in terms of IF-THEN fuzzy rules. Computer simulations are conducted to verify the validity and the performance of the proposed neurofuzzy network for modeling CVD processes.