Feedforward neural networks for estimating IC parametric yield and device characterization

A unique and accurate approach for modeling semiconductor device characteristics and estimating IC parametric yield is described. Multilayer perceptron neural networks (MLPNN) are trained using error back propagation to model DC device characteristics measured at the final fabrication stage. Measurements of material and/or device characteristics taken at earlier fabrication stages are used to develop neural network models of the final DC parameters. A very good agreement has been found between the actual measurements and the MLPNN modeled parameters, and the resulting yield estimations are in excellent agreement with the actual yield