Device modelling for VLSI circuit design with technology independent neural network interface

A novel, fast and accurate neural network tool is proposed for efficient technology independent implementation of the interface between device modelling and circuit simulation. Modified backpropagation, conjugate gradient and Levenberg-Marquardt optimization algorithms are applied in network teaching. Simulations show fast convergence and an excellent fit of recalled characteristics to the measured device data. The utilized algorithms are robust and capable of presenting the entire device characteristics unaltered even with largely reduced amount of the teaching material. The good monotonicity of the neural network generated device data facilitates the usage of the method in circuit simulation purposes. The method is tested against difficult GaAs MESFET and Si MOSFET device data