THRESHOLD VOLTAGE MODELING USING NEURAL NETWORKS

In this paper, threshold voltage modeling based on neural networks is presented. The database was obtained by performing DC analysis with possible combinations of MOSFETs terminal voltages and channel widths which directly effect threshold voltage values, in submicron technology. The neural network was trained with the database including 0.25 mu m and 0.40 mu m TSMC process parameters. In order to prove the extrapolation ability, the test dataset is constituted with 0.18 mu m TSMC process parameters, which were not applied to the neural network for training. The test results of neural network tool are compared with the data obtained by using the Cadence simulation tool. The excellent agreement between the experimental and the model results makes neural networks a powerful tool for estimation of the threshold voltage values.