P. Ojala et al., NOVEL TECHNOLOGY INDEPENDENT NEURAL-NETWORK APPROACH ON DEVICE MODELING INTERFACE, IEE proceedings. Circuits, devices and systems, 142(1), 1995, pp. 74-82
A novel, fast and accurate neural network tool is proposed for efficie
nt technology independent realisation of the interface between device
modelling and circuit simulation. Enhanced back-propagation neural net
work based algorithms are applied to the problem of modelling various
device characteristics. These algorithms include the modified back-pro
pagation algorithm, the conjugate gradient algorithm and the Levenberg
-Marquardt algorithm. Also, the radial basis function neural network i
s tested in the device modelling problem. Simulations show fast conver
gence or learning rate and an excellent fit of recalled characteristic
s to the measured device data. The algorithm utilised is robust and ca
pable of presenting the entire device characteristics unaltered even w
ith largely reduced amount of the teaching material. The good monotoni
city of the neural network generated device data facilitates the usage
of the method in circuit simulation purposes. Possible further applic
ations of implementing circuit level macromodels with this technique a
re discussed.