Vector mapping with a nonlinear electronic layer for distributed neural networks

Abstract

Describes a new approach for obtaining neural network functionality using fully distributed electronic transport rather than lumped electronic circuit elements. For this, vector mapping abilities of a two-dimensional nonlinear inhomogeneous layer are analyzed. This layer is modeled as an inhomogeneous inversion layer in a multiterminal field effect semiconductor device. The author gives computed results as examples of nonlinear vector mapping abilities including nontrivial logic functions with such a layer. These results are achieved by defining relative or differential output signals for the representation of the output information. The type of mapping achieved here is analogous to the one with high-order neural networks. The memory function in the author's structure is imbedded in the distribution of the inhomogeneities.