A HARDWARE IMPLEMENTATION OF AN ANALOG NEURAL NETWORK FOR GAUSSIAN PEAK-FITTING

This paper demonstrates the implementation in hardware of an electrically trainable analog neural network (ETANN) for finding the position and width (FWHM) of an ion-beam hitting a strip-detector. This is accomplished using a single ETANN chip with 32 neurons in one hidden layer. The network finds the maximum and the FWHM, with an error of 0.1 and 0.2, respectively, of the 16 wire input pitch. An extension of this linear peak-fitting problem to include finding the height is presented. Extensions to larger nets with 64 and 128 inputs are presented as multi-chip solutions. A track-finding problem using several chips is briefly discussed.