A resource-allocating network for function interpolation
Neural Computation
A general heuristic for choosing the regularization parameter in ill-posed problems
SIAM Journal on Scientific Computing
A Technique for the Numerical Solution of Certain Integral Equations of the First Kind
Journal of the ACM (JACM)
Regularization in the selection of radial basis function centers
Neural Computation
| Hi-index | 2.88 |
A method for the proximity effects correction in Electron Beam Lithography for layouts with critical dimensions below 180nm is proposed. A parallel processing system based on an artificial neural networks is suggested as a solution to the problem. The synthesis of the architecture as well as the training algorithms for such neurocorrection system are presented. The algorithm for the learning vector generation is based on a discrete iterative regularisation. Several results of the correction process for different test layouts are presented. Error analysis of the error measure is presented. The difference between the target dose and the doses deposited in each exel after the correction process is smaller than 5%. As an hardware implementation of the real time proximity effects corrector the RBF (radial basis functions) neural system is proposed. Simulations of the Gaussian synapse cell have been done. Results of our simulations assure that our neurocorrector can precompensate for one exel from the layout in less than 60ns.