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This paper proposes a wafer alignment method using a feedforward neural network (FNN). A wafer is placed on a vacuum table and its misalignment is inspected by a frame grabber. The alignment of the wafer is repeatedly corrected by adjusting its kinematic positions until the misalignment becomes zero. The training set is composed of the misalignment and the xy?? compensation. Fifteen sets of misalignment data were measured by the conventional alignment method. The FNN has four normalized inputs as mark locations in the field of vision and three normalized outputs for the kinematic compensation. The macro and micro steps share the FNN by means of a multiplexer (MUX) and a scaler. The training rule was back-propagation (BP). The proposed method was applied to a wafer manufacturing machine and its alignment performance was compared with that of conventional methods. The alignment time was reduced by the FNN model which was especially efficient in the case of macroscopic alignment.