A GA-PNN was constructed and applied to a model plasma etch process. The etch process was characterised by a statistical experimental design. The performance of GA-PNN was compared with other models including the three types of statistical regression models, conventional backpropagation neural network, polynomial neural network and adaptive network based fuzzy inference system models. In all comparisons, the GA-PNN demonstrated signi cantly improved predictions. Moreover, the network complexity of conventional PNN could be signi cantly reduced. This indicates that the GA-PNN is an effective means to construct a predictive model for poorly de ned complex systems characterised by the limited data set. SE/S276 Drs Kim and Park are in theDepartment INTRODUCTIONPlasma etching is a key means of forming ne patterns for manufacturing integrated circuits.Owing to complex chemical reactions between plasma variables, ions and radicals, and material surfaces, it has been extremely dif cult to model plasma etching. There have been many reports on constructing predictive etch models using intelligent systems such as backpropagation neural network (BPNN) 1 -3 and fuzzy logic. 4 Recently, a polynomial neural network (PNN) 5 was applied and demonstrated improved prediction over conventional statistical regression models or BPNN. Despite the advantages, constructing a PNN model is complicated by the presence of several training factors, whose optimal values are initially unknown. These may include the number of partial descriptions (PDs) in each layer, the number of input variables connected to each PD, or the order of polynomials assignable to each PD. 6 In most cases, they are determined by the trial and error method, thereby causing a heavy computational burden and low ef ciency. Moreover, the heuristic nature of the PNN algorithm does not guarantee that the PNN model constructed has the best predictive ability. In this paper, a method of circumventing the drawbacks of current PNNs is presented. This is accomplished using a genetic algorithm (GA) 7 to optimise the training factors. For convenience, this type of PNN is referred to as a 'GA-PNN'. The GA is used to search for one optimal set of training factors, including the number of input variables to each PD, the selection of input variables and the type of polynomials of PDs. The performance of the GA-PNN is compared with statistical regression models, conventional BPNN and PNN, and adaptive networks based fuzzy inference system (ANFIS) models. Model
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